{
      "date_type": "completed",
      "creators": [
        {
          "name": {
            "lineage": null,
            "given": "Alief Cahyo Utomo",
            "family": "Alief Cahyo Utomo",
            "honourific": null
          },
          "id": 2113030101
        }
      ],
      "department": "KODEPRODI57201#Sistem Informasi",
      "date": "2025-12-29",
      "divisions": [
        "sch_med"
      ],
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      "institution": "Universitas Nusantara PGRI Kediri",
      "pages": 60,
      "full_text_status": "restricted",
      "dir": "disk0\/00\/02\/32\/12",
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      "abstract": "Penelitian ini bertujuan untuk menganalisis efektivitas\r\nmetode Halving Random Search Cross Validation sebagai alternatif optimasi\r\nhyperparameter pada model machine learning dibandingkan dengan Grid\r\nSearch Cross Validation dan Random Search Cross Validation. Dataset yang\r\ndigunakan adalah Internet Service Churn dengan empat algoritma: KNN,\r\nDecision Tree, SVM, dan Gaussian Naive Bayes. Proses pengujian melibatkan\r\n10-fold cross validation dan pengulangan tiga kali untuk memastikan validitas\r\nhasil.\r\nHasil eksperimen menunjukkan bahwa Halving Random Search Cross\r\nValidation mampu mencapai performa akurasi, presisi, dan recall yang\r\nkompetitif (selisih < 0,5%) dibandingkan Grid Search pada sebagian besar\r\nmodel, dengan penghematan waktu komputasi hingga 62–74% pada KNN,\r\nDecision Tree, dan SVM. Namun, pada Gaussian Naive Bayes dengan ruang\r\nhyperparameter kecil, metode ini lebih lambat karena overhead successive\r\nhalving. Random Search menunjukkan kecepatan tinggi tetapi kurang stabil\r\npada SVM dan Gaussian Naive Bayes.\r\nKesimpulan penelitian menyatakan bahwa Halving Random Search Cross\r\nValidation adalah metode paling seimbang untuk kasus bisnis seperti prediksi\r\nchurn, dengan rekomendasi penerapan pada model kompleks dan\r\npengembangan lanjutan menggunakan Hyperband atau Bayesian Optimization.",
      "type": "thesis",
      "status_changed": "2026-03-02 10:03:05",
      "subjects": [
        459,
        461
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      "title": "ANALISIS WAKTU OPTIMASI VALIDASI SILANG PENCARIAN ACAK\r\nBERTAHAP PADA MODEL PEMBELAJARAN MESIN",
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      "referencetext": "Nurjanah, I., Karaman, J., Widaningrum, I., Mustikasari, D., & Sucipto. (2023).\r\nPenggunaan algoritma Naive Bayes untuk menentukan pemberian kredit pada\r\nkoperasi desa. Journal of Computer Science and Information Technology, 3(2).\r\nhttps:\/\/doi.org\/10.47065\/explorer.v3i2.766\r\nSucipto, Prasetya, D. D., & Widiyaningtyas, T. (2025). A supervised hybrid\r\nweighting scheme for Bloom’s taxonomy questions using category space\r\ndensity-based weighting. Engineering, Technology and Applied Science Research,\r\n15(2), 22102–22108. https:\/\/doi.org\/10.48084\/etasr.10226\r\nNila, U., Firliana, R., & Sucipto. (2023). Analisis data transaksi penjualan produk\r\npertanian menggunakan algoritma FP-Growth. Prosiding Seminar Nasional\r\nInovasi Teknologi (SEMNAS INOTEK), 7.\r\nhttps:\/\/doi.org\/10.29407\/inotek.v7i1.3426\r\nGusti Tammam, A., Indriati, R., & Sucipto. (2018). Hoax detection at social\r\nmedia with text mining clarification system-based. Jurnal Ilmiah Pengembangan\r\nInformatika, 3(2). https:\/\/doi.org\/10.29100\/jipi.v3i2.837\r\nAnggraini, I. Y., Sucipto, S., & Indriati, R. (2018). Cyberbullying detection\r\nmodelling at Twitter social networking. JUITA: Jurnal Informatika, 6(2),\r\n113–118. https:\/\/doi.org\/10.30595\/juita.v6i2.3350\r\nAprilliandhika, W., & Abdulloh, F. F. (2024). Comparison of K-nearest neighbor\r\nand support vector machine algorithm optimization with grid search CV on stroke\r\nprediction. Jurnal Teknologi Informasi dan Ilmu Komputer, 5(4), 991–1000.\r\nhttps:\/\/doi.org\/10.52436\/1.jutif.2024.5.4.1951\r\nDirjen, S. K., Irmanda, H. N., & Astriratma, R. (2017). Klasifikasi jenis pantun\r\ndengan metode support vector machines (SVM). Jurnal RESTI (Rekayasa Sistem\r\ndan Teknologi Informasi), 1(3), 915–922. https:\/\/doi.org\/10.29207\/resti.v4i5.2313\r\nIrawan, I., Qisthiano, R., Syahril, M., & Jakak, P. M. (2023). Optimasi prediksi\r\nkelulusan tepat waktu: Studi perbandingan algoritma random forest dan K-NN\r\n54\r\n55\r\nberbasis PSO. Jurnal Pengembangan Sistem Informasi dan Informatika, 4(4).\r\nhttps:\/\/doi.org\/10.47747\/jpsii.v4i4.1374\r\nJamiluddin, F., Faisal, S., Lestari, S. A. P., & Fauzi, A. (2024). Implementasi\r\nhyperparameter tuning grid search CV pada prediksi produksi padi menggunakan\r\nalgoritma linear regresi. Journal of Information System Research, 6(1), 490–498.\r\nhttps:\/\/doi.org\/10.47065\/josh.v6i1.5930\r\nMisnawati. (2023). ChatGPT: Keuntungan, risiko, dan penggunaan bijak dalam\r\nera kecerdasan buatan. Prosiding Mateandrau, 2(1).\r\nhttps:\/\/doi.org\/10.55606\/mateandrau.v2i1.221\r\nMuhamad, I., & Matin, M. (2023). Hyperparameter tuning menggunakan\r\nGridSearchCV pada random forest untuk deteksi malware. Multinetics, 9(1).\r\nhttps:\/\/doi.org\/10.32722\/multinetics.v9i1.5578\r\nMunawaroh, S., Rosyidah, U. A., & Yanuarti, R. (2024). Klasifikasi tingkat\r\nkecemasan atlet sebelum bertanding menggunakan algoritma K-nearest neighbor\r\n(KNN) berbasis website. BIOS: Jurnal Teknologi Informasi dan Rekayasa\r\nKomputer, 5(2), 87–94. https:\/\/doi.org\/10.37148\/bios.v5i2.120\r\nNugraha, W., & Sasongko, A. (2022). Hyperparameter tuning pada algoritma\r\nklasifikasi dengan grid search. SISTEMASI: Jurnal Sistem Informasi, 11(2).\r\nhttps:\/\/doi.org\/10.32520\/stmsi.v11i2.1750\r\nNugroho, A., Soeleman, M. A., Pramunendar, R. A., & Nurhindarto, A. (2023).\r\nPeningkatan performa ensemble learning pada segmentasi semantik gambar\r\ndengan teknik oversampling untuk class imbalance. Jurnal Teknologi Informasi\r\ndan Ilmu Komputer. https:\/\/doi.org\/10.25126\/jtiik.2023106831\r\nPutri, T. A. E., Widiharih, T., & Santoso, R. (2023). Penerapan tuning\r\nhyperparameter RandomSearchCV pada adaptive boosting untuk prediksi\r\nkelangsungan hidup pasien gagal jantung. Jurnal Gaussian, 11(3), 397–406.\r\nhttps:\/\/doi.org\/10.14710\/j.gauss.11.3.397-406\r\nRahmat, A., Syafiih, M., & Faid, M. (2023). Implementasi klasifikasi potensi\r\n56\r\npenyakit jantung menggunakan metode C4.5 berbasis website (studi kasus\r\nKaggle). INFOTECH Journal, 9(2), 393–400.\r\nhttps:\/\/doi.org\/10.31949\/infotech.v9i2.6295\r\nSartika, D., & Sensuse, D. I. (2017). Perbandingan algoritma klasifikasi Naive\r\nBayes, nearest neighbour, dan decision tree pada studi kasus pengambilan\r\nkeputusan pemilihan pola pakaian. JATISI, 3(2).\r\nhttps:\/\/doi.org\/10.35957\/jatisi.v3i2.78\r\nSoper, D. S. (2023). Hyperparameter optimization using successive halving with\r\ngreedy cross validation. Algorithms, 16(1). https:\/\/doi.org\/10.3390\/a16010017\r\nWijiyanto, W., Pradana, A. I., Sopingi, S., & Atina, V. (2024). Teknik K-fold cross\r\nvalidation untuk mengevaluasi kinerja mahasiswa. Jurnal Algoritma, 21(1).\r\nhttps:\/\/doi.org\/10.33364\/algoritma\/v21-1.1618\r\nSchmucker, R., Donini, M., Zafar, M. B., Salinas, D., & Archambeau, C. (2021).\r\nMulti-objective asynchronous successive halving. arXiv.\r\nhttps:\/\/doi.org\/10.48550\/arXiv.2106.12639\r\nKoyamada, S., Nishimori, S., & Ishii, S. (2024). A batch sequential halving\r\nalgorithm without performance degradation. arXiv.\r\nhttps:\/\/doi.org\/10.48550\/arXiv.2406.00424\r\nBergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2012). Random search for\r\nhyper-parameter optimization. Journal of Machine Learning Research, 13,\r\n281–305.\r\nLi, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2018).\r\nHyperband: A novel bandit-based approach to hyperparameter optimization.\r\nJournal of Machine Learning Research, 18(185), 1–52.\r\nGéron, A. (2019). Hands-on machine learning with Scikit-Learn, Keras, and\r\nTensorFlow (2nd ed.). O’Reilly Media.\r\n57\r\nHan, J., Kamber, M., & Pei, J. (2012). Data mining: Concepts and techniques (3rd\r\ned.). Morgan Kaufmann.\r\nAriwibowo, T. (2019). Perbandingan metode imputasi mean, median, modus, dan\r\n1-NN pada hasil klasifikasi K-nearest neighbour (Skripsi). Universitas\r\nPembangunan Nasional Veteran JakNurjanah, I., Karaman, J., Widaningrum, I., Mustikasari, D., & Sucipto. (2023).\r\nPenggunaan algoritma Naive Bayes untuk menentukan pemberian kredit pada\r\nkoperasi desa.\r\n \r\nJournal of Computer Science and Information Technology\r\n,\r\n \r\n3\r\n(2).\r\nhttps:\/\/doi.org\/10.47065\/explorer.v3i2.766\r\n\r\nSucipto, Prasetya, D. D., & Widiyaningtyas, T. (2025). A supervised hybrid\r\nweighting\r\n \r\nscheme\r\n \r\nfor\r\n \r\nBloom’s\r\n \r\ntaxonomy\r\n \r\nquestions\r\n \r\nusing\r\n \r\ncategory\r\n \r\nspace\r\ndensity-based weighting.\r\n \r\nEngineering, Technology and Applied Science Research\r\n,\r\n15\r\n(2), 22102–22108. https:\/\/doi.org\/10.48084\/etasr.10226\r\n\r\nNila, U., Firliana, R., & Sucipto. (2023). Analisis data transaksi penjualan produk\r\npertanian\r\n \r\nmenggunakan\r\n \r\nalgoritma\r\n \r\nFP-Growth.\r\n \r\nProsiding\r\n \r\nSeminar\r\n \r\nNasional\r\nInovasi\r\n \r\nTeknologi\r\n \r\n(SEMNAS\r\n \r\nINOTEK)\r\n,\r\n \r\n7\r\n.\r\nhttps:\/\/doi.org\/10.29407\/inotek.v7i1.3426\r\n\r\nGusti Tammam, A., Indriati, R., & Sucipto. (2018). Hoax detection at social\r\nmedia with text mining clarification system-based.\r\n \r\nJurnal Ilmiah Pengembangan\r\nInformatika\r\n,\r\n \r\n3\r\n(2). https:\/\/doi.org\/10.29100\/jipi.v3i2.837\r\n\r\nAnggraini, I. Y., Sucipto, S., & Indriati, R. (2018). Cyberbullying detection\r\nmodelling\r\n \r\nat\r\n \r\nTwitter\r\n \r\nsocial\r\n \r\nnetworking.\r\n \r\nJUITA:\r\n \r\nJurnal\r\n \r\nInformatika\r\n,\r\n \r\n6\r\n(2),\r\n113–118. https:\/\/doi.org\/10.30595\/juita.v6i2.3350\r\n\r\nAprilliandhika, W., & Abdulloh, F. F. (2024). Comparison of K-nearest neighbor\r\nand support vector machine algorithm optimization with grid search CV on stroke\r\nprediction.\r\n \r\nJurnal Teknologi Informasi dan Ilmu Komputer\r\n,\r\n \r\n5\r\n(4), 991–1000.\r\nhttps:\/\/doi.org\/10.52436\/1.jutif.2024.5.4.1951\r\n\r\nDirjen, S. K., Irmanda, H. N., & Astriratma, R. (2017). Klasifikasi jenis pantun\r\ndengan metode support vector machines (SVM).\r\n \r\nJurnal RESTI (Rekayasa Sistem\r\ndan Teknologi Informasi)\r\n,\r\n \r\n1\r\n(3), 915–922. https:\/\/doi.org\/10.29207\/resti.v4i5.2313\r\n\r\nIrawan, I., Qisthiano, R., Syahril, M., & Jakak, P. M. (2023). Optimasi prediksi\r\nkelulusan tepat waktu: Studi perbandingan algoritma random forest dan K-NN\r\n54\r\n55\r\nberbasis PSO.\r\n \r\nJurnal Pengembangan Sistem Informasi dan Informatika\r\n,\r\n \r\n4\r\n(4).\r\nhttps:\/\/doi.org\/10.47747\/jpsii.v4i4.1374\r\n\r\nJamiluddin, F., Faisal, S., Lestari, S. A. P., & Fauzi, A. (2024). Implementasi\r\nhyperparameter tuning grid search CV pada prediksi produksi padi menggunakan\r\nalgoritma linear regresi.\r\n \r\nJournal of Information System Research\r\n,\r\n \r\n6\r\n(1), 490–498.\r\nhttps:\/\/doi.org\/10.47065\/josh.v6i1.5930\r\n\r\nMisnawati. (2023). ChatGPT: Keuntungan, risiko, dan penggunaan bijak dalam\r\nera\r\n \r\nkecerdasan\r\n \r\nbuatan.\r\n \r\nProsiding\r\n \r\nMateandrau\r\n,\r\n \r\n2\r\n(1).\r\nhttps:\/\/doi.org\/10.55606\/mateandrau.v2i1.221\r\n\r\nMuhamad,\r\n \r\nI.,\r\n \r\n&\r\n \r\nMatin,\r\n \r\nM.\r\n \r\n(2023).\r\n \r\nHyperparameter\r\n \r\ntuning\r\n \r\nmenggunakan\r\nGridSearchCV pada random forest untuk deteksi malware.\r\n \r\nMultinetics\r\n,\r\n \r\n9\r\n(1).\r\nhttps:\/\/doi.org\/10.32722\/multinetics.v9i1.5578\r\n\r\nMunawaroh, S., Rosyidah, U. A., & Yanuarti, R. (2024). Klasifikasi tingkat\r\nkecemasan atlet sebelum bertanding menggunakan algoritma K-nearest neighbor\r\n(KNN)\r\n \r\nberbasis\r\n \r\nwebsite.\r\n \r\nBIOS:\r\n \r\nJurnal\r\n \r\nTeknologi\r\n \r\nInformasi\r\n \r\ndan\r\n \r\nRekayasa\r\nKomputer\r\n,\r\n \r\n5\r\n(2), 87–94. https:\/\/doi.org\/10.37148\/bios.v5i2.120\r\n\r\nNugraha, W., & Sasongko, A. (2022). Hyperparameter tuning pada algoritma\r\nklasifikasi\r\n \r\ndengan\r\n \r\ngrid\r\n \r\nsearch.\r\n \r\nSISTEMASI:\r\n \r\nJurnal Sistem Informasi\r\n,\r\n \r\n11\r\n(2).\r\nhttps:\/\/doi.org\/10.32520\/stmsi.v11i2.1750\r\n\r\nNugroho, A., Soeleman, M. A., Pramunendar, R. A., & Nurhindarto, A. (2023).\r\nPeningkatan\r\n \r\nperforma\r\n \r\nensemble\r\n \r\nlearning\r\n \r\npada\r\n \r\nsegmentasi\r\n \r\nsemantik\r\n \r\ngambar\r\ndengan teknik oversampling untuk class imbalance.\r\n \r\nJurnal Teknologi Informasi\r\ndan Ilmu Komputer\r\n. https:\/\/doi.org\/10.25126\/jtiik.2023106831\r\n\r\nPutri,\r\n \r\nT.\r\n \r\nA.\r\n \r\nE.,\r\n \r\nWidiharih,\r\n \r\nT.,\r\n \r\n&\r\n \r\nSantoso,\r\n \r\nR.\r\n \r\n(2023).\r\n \r\nPenerapan\r\n \r\ntuning\r\nhyperparameter\r\n \r\nRandomSearchCV\r\n \r\npada\r\n \r\nadaptive\r\n \r\nboosting\r\n \r\nuntuk\r\n \r\nprediksi\r\nkelangsungan\r\n \r\nhidup pasien gagal jantung.\r\n \r\nJurnal Gaussian\r\n,\r\n \r\n11\r\n(3), 397–406.\r\nhttps:\/\/doi.org\/10.14710\/j.gauss.11.3.397-406\r\n\r\nRahmat, A., Syafiih, M., & Faid, M. (2023). Implementasi klasifikasi potensi\r\n56\r\npenyakit\r\n \r\njantung\r\n \r\nmenggunakan\r\n \r\nmetode\r\n \r\nC4.5\r\n \r\nberbasis\r\n \r\nwebsite\r\n \r\n(studi\r\n \r\nkasus\r\nKaggle).\r\n \r\nINFOTECH\r\n \r\nJournal\r\n,\r\n \r\n9\r\n(2),\r\n \r\n393–400.\r\nhttps:\/\/doi.org\/10.31949\/infotech.v9i2.6295\r\n\r\nSartika, D., & Sensuse, D. I. (2017). Perbandingan algoritma klasifikasi Naive\r\nBayes,\r\n \r\nnearest\r\n \r\nneighbour,\r\n \r\ndan\r\n \r\ndecision\r\n \r\ntree\r\n \r\npada\r\n \r\nstudi\r\n \r\nkasus\r\n \r\npengambilan\r\nkeputusan\r\n \r\npemilihan\r\n \r\npola\r\n \r\npakaian.\r\n \r\nJATISI\r\n,\r\n \r\n3\r\n(2).\r\nhttps:\/\/doi.org\/10.35957\/jatisi.v3i2.78\r\n\r\nSoper, D. S. (2023). Hyperparameter optimization using successive halving with\r\ngreedy cross validation.\r\n \r\nAlgorithms\r\n,\r\n \r\n16\r\n(1). https:\/\/doi.org\/10.3390\/a16010017\r\n\r\nWijiyanto, W., Pradana, A. I., Sopingi, S., & Atina, V. (2024). Teknik K-fold cross\r\nvalidation\r\n \r\nuntuk\r\n \r\nmengevaluasi\r\n \r\nkinerja\r\n \r\nmahasiswa.\r\n \r\nJurnal\r\n \r\nAlgoritma\r\n,\r\n \r\n21\r\n(1).\r\nhttps:\/\/doi.org\/10.33364\/algoritma\/v21-1.1618\r\n\r\nSchmucker, R., Donini, M., Zafar, M. B., Salinas, D., & Archambeau, C. (2021).\r\nMulti-objective\r\n \r\nasynchronous\r\n \r\nsuccessive\r\n \r\nhalving.\r\n \r\narXiv\r\n.\r\nhttps:\/\/doi.org\/10.48550\/arXiv.2106.12639\r\n\r\nKoyamada, S., Nishimori, S., & Ishii, S. (2024). A batch sequential halving\r\nalgorithm\r\n \r\nwithout\r\n \r\nperformance\r\n \r\ndegradation.\r\n \r\narXiv\r\n.\r\nhttps:\/\/doi.org\/10.48550\/arXiv.2406.00424\r\n\r\nBergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2012). Random search for\r\nhyper-parameter\r\n \r\noptimization.\r\n \r\nJournal\r\n \r\nof\r\n \r\nMachine\r\n \r\nLearning\r\n \r\nResearch\r\n,\r\n \r\n13\r\n,\r\n281–305.\r\n\r\nLi, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. (2018).\r\nHyperband:\r\n \r\nA\r\n \r\nnovel\r\n \r\nbandit-based\r\n \r\napproach\r\n \r\nto hyperparameter optimization.\r\nJournal of Machine Learning Research\r\n,\r\n \r\n18\r\n(185), 1–52.\r\n\r\nGéron, A. (2019).\r\n \r\nHands-on machine learning with Scikit-Learn, Keras, and\r\nTensorFlow\r\n \r\n(2nd ed.). O’Reilly Media.\r\n57\r\nHan, J., Kamber, M., & Pei, J. (2012).\r\n \r\nData mining: Concepts and techniques\r\n \r\n(3rd\r\ned.). Morgan Kaufmann.\r\n\r\nAriwibowo, T. (2019).\r\n \r\nPerbandingan metode imputasi mean, median, modus, dan\r\n1-NN\r\n \r\npada\r\n \r\nhasil\r\n \r\nklasifikasi\r\n \r\nK-nearest\r\n \r\nneighbour\r\n \r\n(Skripsi).\r\n \r\nUniversitas\r\nPembangunan\r\n \r\nNasional\r\n \r\nVeteran\r\n \r\nJakarta.\r\nhttps:\/\/repository.upnvj.ac.id\/1346\/1\/AWAL.pdf\r\n\r\nAbdelAziz, N. M., Bekheet, M., Salah, A., El-Saber, N., & AbdelMoneim, W. T.\r\n(2025). A comprehensive evaluation of machine learning and deep learning\r\n\r\nmodels\r\n \r\nfor\r\n \r\nchurn\r\n \r\nprediction.\r\n \r\nInformation\r\n,\r\n \r\n16(7),\r\n \r\n537.\r\n\r\nhttps:\/\/doi.org\/10.3390\/info16070537\r\n\r\nBaskan, M. (2023).\r\n \r\nA machine learning framework to address customer churn\r\nproblem using uplift modelling and prescriptive analysis\r\n \r\n[Tesis]. National College\r\n\r\nof Ireland.\r\n \r\nhttps:\/\/norma.ncirl.ie\/6081\/1\/mervebaskan.pdfarta.\r\nhttps:\/\/repository.upnvj.ac.id\/1346\/1\/AWAL.pdf\r\nAbdelAziz, N. M., Bekheet, M., SaNurjanah, I., Karaman, J., Widaningrum, I., Mustikasari, D., & Sucipto. (2023).\r\nPenggunaan algoritma Naive Bayes untuk menentukan pemberian kredit pada\r\nkoperasi desa.\r\n \r\nJournal of Computer Science and Information Technology\r\n,\r\n \r\n3\r\n(2).\r\nhttps:\/\/doi.org\/10.47065\/explorer.v3i2.766\r\n\r\nSucipto, Prasetya, D. D., & Widiyaningtyas, T. (2025). A supervised hybrid\r\nweighting\r\n \r\nscheme\r\n \r\nfor\r\n \r\nBloom’s\r\n \r\ntaxonomy\r\n \r\nquestions\r\n \r\nusing\r\n \r\ncategory\r\n \r\nspace\r\ndensity-based weighting.\r\n \r\nEngineering, Technology and Applied Science Research\r\n,\r\n15\r\n(2), 22102–22108. https:\/\/doi.org\/10.48084\/etasr.10226\r\n\r\nNila, U., Firliana, R., & Sucipto. (2023). Analisis data transaksi penjualan produk\r\npertanian\r\n \r\nmenggunakan\r\n \r\nalgoritma\r\n \r\nFP-Growth.\r\n \r\nProsiding\r\n \r\nSeminar\r\n \r\nNasional\r\nInovasi\r\n \r\nTeknologi\r\n \r\n(SEMNAS\r\n \r\nINOTEK)\r\n,\r\n \r\n7\r\n.\r\nhttps:\/\/doi.org\/10.29407\/inotek.v7i1.3426\r\n\r\nGusti Tammam, A., Indriati, R., & Sucipto. (2018). Hoax detection at social\r\nmedia with text mining clarification system-based.\r\n \r\nJurnal Ilmiah Pengembangan\r\nInformatika\r\n,\r\n \r\n3\r\n(2). https:\/\/doi.org\/10.29100\/jipi.v3i2.837\r\n\r\nAnggraini, I. Y., Sucipto, S., & Indriati, R. (2018). Cyberbullying detection\r\nmodelling\r\n \r\nat\r\n \r\nTwitter\r\n \r\nsocial\r\n \r\nnetworking.\r\n \r\nJUITA:\r\n \r\nJurnal\r\n \r\nInformatika\r\n,\r\n \r\n6\r\n(2),\r\n113–118. https:\/\/doi.org\/10.30595\/juita.v6i2.3350\r\n\r\nAprilliandhika, W., & Abdulloh, F. F. (2024). Comparison of K-nearest neighbor\r\nand support vector machine algorithm optimization with grid search CV on stroke\r\nprediction.\r\n \r\nJurnal Teknologi Informasi dan Ilmu Komputer\r\n,\r\n \r\n5\r\n(4), 991–1000.\r\nhttps:\/\/doi.org\/10.52436\/1.jutif.2024.5.4.1951\r\n\r\nDirjen, S. K., Irmanda, H. N., & Astriratma, R. (2017). Klasifikasi jenis pantun\r\ndengan metode support vector machines (SVM).\r\n \r\nJurnal RESTI (Rekayasa Sistem\r\ndan Teknologi Informasi)\r\n,\r\n \r\n1\r\n(3), 915–922. https:\/\/doi.org\/10.29207\/resti.v4i5.2313\r\n\r\nIrawan, I., Qisthiano, R., Syahril, M., & Jakak, P. M. (2023). Optimasi prediksi\r\nkelulusan tepat waktu: Studi perbandingan algoritma random forest dan K-NN\r\n54\r\n55\r\nberbasis PSO.\r\n \r\nJurnal Pengembangan Sistem Informasi dan Informatika\r\n,\r\n \r\n4\r\n(4).\r\nhttps:\/\/doi.org\/10.47747\/jpsii.v4i4.1374\r\n\r\nJamiluddin, F., Faisal, S., Lestari, S. A. P., & Fauzi, A. (2024). Implementasi\r\nhyperparameter tuning grid search CV pada prediksi produksi padi menggunakan\r\nalgoritma linear regresi.\r\n \r\nJournal of Information System Research\r\n,\r\n \r\n6\r\n(1), 490–498.\r\nhttps:\/\/doi.org\/10.47065\/josh.v6i1.5930\r\n\r\nMisnawati. (2023). ChatGPT: Keuntungan, risiko, dan penggunaan bijak dalam\r\nera\r\n \r\nkecerdasan\r\n \r\nbuatan.\r\n \r\nProsiding\r\n \r\nMateandrau\r\n,\r\n \r\n2\r\n(1).\r\nhttps:\/\/doi.org\/10.55606\/mateandrau.v2i1.221\r\n\r\nMuhamad,\r\n \r\nI.,\r\n \r\n&\r\n \r\nMatin,\r\n \r\nM.\r\n \r\n(2023).\r\n \r\nHyperparameter\r\n \r\ntuning\r\n \r\nmenggunakan\r\nGridSearchCV pada random forest untuk deteksi malware.\r\n \r\nMultinetics\r\n,\r\n \r\n9\r\n(1).\r\nhttps:\/\/doi.org\/10.32722\/multinetics.v9i1.5578\r\n\r\nMunawaroh, S., Rosyidah, U. A., & Yanuarti, R. (2024). Klasifikasi tingkat\r\nkecemasan atlet sebelum bertanding menggunakan algoritma K-nearest neighbor\r\n(KNN)\r\n \r\nberbasis\r\n \r\nwebsite.\r\n \r\nBIOS:\r\n \r\nJurnal\r\n \r\nTeknologi\r\n \r\nInformasi\r\n \r\ndan\r\n \r\nRekayasa\r\nKomputer\r\n,\r\n \r\n5\r\n(2), 87–94. https:\/\/doi.org\/10.37148\/bios.v5i2.120\r\n\r\nNugraha, W., & Sasongko, A. (2022). Hyperparameter tuning pada algoritma\r\nklasifikasi\r\n \r\ndengan\r\n \r\ngrid\r\n \r\nsearch.\r\n \r\nSISTEMASI:\r\n \r\nJurnal Sistem Informasi\r\n,\r\n \r\n11\r\n(2).\r\nhttps:\/\/doi.org\/10.32520\/stmsi.v11i2.1750\r\n\r\nNugroho, A., Soeleman, M. A., Pramunendar, R. A., & Nurhindarto, A. (2023).\r\nPeningkatan\r\n \r\nperforma\r\n \r\nensemble\r\n \r\nlearning\r\n \r\npada\r\n \r\nsegmentasi\r\n \r\nsemantik\r\n \r\ngambar\r\ndengan teknik oversampling untuk class imbalance.\r\n \r\nJurnal Teknologi Informasi\r\ndan Ilmu Komputer\r\n. https:\/\/doi.org\/10.25126\/jtiik.2023106831\r\n\r\nPutri,\r\n \r\nT.\r\n \r\nA.\r\n \r\nE.,\r\n \r\nWidiharih,\r\n \r\nT.,\r\n \r\n&\r\n \r\nSantoso,\r\n \r\nR.\r\n \r\n(2023).\r\n \r\nPenerapan\r\n \r\ntuning\r\nhyperparameter\r\n \r\nRandomSearchCV\r\n \r\npada\r\n \r\nadaptive\r\n \r\nboosting\r\n \r\nuntuk\r\n \r\nprediksi\r\nkelangsungan\r\n \r\nhidup pasien gagal jantung.\r\n \r\nJurnal Gaussian\r\n,\r\n \r\n11\r\n(3), 397–406.\r\nhttps:\/\/doi.org\/10.14710\/j.gauss.11.3.397-406\r\n\r\nRahmat, A., Syafiih, M., & Faid, M. (2023). Implementasi klasifikasi potensi\r\n56\r\npenyakit\r\n \r\njantung\r\n \r\nmenggunakan\r\n \r\nmetode\r\n \r\nC4.5\r\n \r\nberbasis\r\n \r\nwebsite\r\n \r\n(studi\r\n \r\nkasus\r\nKaggle).\r\n \r\nINFOTECH\r\n \r\nJournal\r\n,\r\n \r\n9\r\n(2),\r\n \r\n393–400.\r\nhttps:\/\/doi.org\/10.31949\/infotech.v9i2.6295\r\n\r\nSartika, D., & Sensuse, D. I. (2017). Perbandingan algoritma klasifikasi Naive\r\nBayes,\r\n \r\nnearest\r\n \r\nneighbour,\r\n \r\ndan\r\n \r\ndecision\r\n \r\ntree\r\n \r\npada\r\n \r\nstudi\r\n \r\nkasus\r\n \r\npengambilan\r\nkeputusan\r\n \r\npemilihan\r\n \r\npola\r\n \r\npakaian.\r\n \r\nJATISI\r\n,\r\n \r\n3\r\n(2).\r\nhttps:\/\/doi.org\/10.35957\/jatisi.v3i2.78\r\n\r\nSoper, D. S. (2023). Hyperparameter optimization using successive halving with\r\ngreedy cross validation.\r\n \r\nAlgorithms\r\n,\r\n \r\n16\r\n(1). https:\/\/doi.org\/10.3390\/a16010017\r\n\r\nWijiyanto, W., Pradana, A. I., Sopingi, S., & Atina, V. (2024). Teknik K-fold cross\r\nvalidation\r\n \r\nuntuk\r\n \r\nmengevaluasi\r\n \r\nkinerja\r\n \r\nmahasiswa.\r\n \r\nJurnal\r\n \r\nAlgoritma\r\n,\r\n \r\n21\r\n(1).\r\nhttps:\/\/doi.org\/10.33364\/algoritma\/v21-1.1618\r\n\r\nSchmucker, R., Donini, M., Zafar, M. B., Salinas, D., & Archambeau, C. (2021).\r\nMulti-objective\r\n \r\nasynchronous\r\n \r\nsuccessive\r\n \r\nhalving.\r\n \r\narXiv\r\n.\r\nhttps:\/\/doi.org\/10.48550\/arXiv.2106.12639\r\n\r\nKoyamada, S., Nishimori, S., & Ishii, S. (2024). A batch sequential halving\r\nalgorithm\r\n \r\nwithout\r\n \r\nperformance\r\n \r\ndegradation.\r\n \r\narXiv\r\n.\r\nhttps:\/\/doi.org\/10.48550\/arXiv.2406.00424\r\n\r\nBergstra, J., Bardenet, R., Bengio, Y., & Kégl, B. (2012). Random search for\r\nhyper-parameter\r\n \r\noptimization.\r\n \r\nJournal\r\n \r\nof\r\n \r\nMachine\r\n \r\nLearning\r\n \r\nResearch\r\n,\r\n \r\n13\r\n,\r\n281–305.\r\n\r\nLi, L., Jamieson, K., DeSalvo, G., Rostamizadeh, A., & Talwalkar, A. 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    }