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All Journal JOINCS (Journal of Informatics, Network, and Computer Science) Jurnal Pengabdian Masyarakat Jurnal Ilmu Komputer dan Matematika
Widya Cholid Wahyudin
Universitas Muhammadiyah Kudus
Religion Computer Science & IT Control & Systems Engineering Education Library & Information Science Mathematics Social Sciences

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Journal : joincs journal of informatics network and computer science

 1 
On-Time Student Graduation Prediction Modeling: A Comparative Analysis of Naive Bayes Algorithm and Other Data Mining Classifications: Pemodelan Prediksi Kelulusan Mahasiswa Tepat Waktu: Analisis Komparatif Algoritma Naive Bayes Dan Klasifikasi Data Mining Lainnya Achmad Ridwan; Tole Sutikno; Imam Riyadi; Widya Cholid Wahyudin
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 2 (2025): November
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i2.1679

Abstract

Predicting the on-time graduation of university students is a crucial task in higher education institutions, enabling proactive support and improving institutional effectiveness. This paper presents a comparative analysis of several machine learning algorithms for predicting on-time graduation, with a specific focus on challenging the performance of the Naive Bayes (NB) algorithm. Although often used as a baseline model, the effectiveness of NB in the complex domain of educational data is frequently debated. We compare NB with MultinomialNB and Decision Tree (DT), both widely favored in recent literature. Using a public dataset containing students' academic records, we follow the CRISP-DM methodology, incorporating feature selection and SMOTE to address class imbalance. The models are evaluated using accuracy, precision, recall, and F1-score metrics. Our results show that while Decision Tree achieves the highest accuracy, Naive Bayes offers an appealing balance of performance, computational efficiency, and interpretability, making it a strong candidate for implementation in early warning systems at universities. This study provides empirical evidence on the role of Naive Bayes in the current landscape of educational data mining. The classification results show an accuracy of 0.82 for Naive Bayes, 0.81 for MultinomialNB, and 0.85 for Decision Tree.
Identification of Bengawan Solo River Water Quality Patterns Using K-Means Clustering Based on Physicochemical and Environmental Parameters: Identifikasi Pola Kualitas Air Sungai Bengawan Solo Menggunakan Klasterisasi K-Means Berdasarkan Parameter Fisik-Kimia dan Lingkungan Widya Cholid Wahyudin; Tole Sutikno; Rusydi Umar; Widya Cholid Wahyudin
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 9 No. 1 (2026): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v9i1.1710

Abstract

Abstract. River water quality needs to be monitored continuously because changes in physicochemical and environmental parameters may indicate early changes in aquatic conditions. This study aims to identify water quality patterns in the Bengawan Solo River using K-Means clustering based on physicochemical and environmental parameters. The dataset consists of 1,753 field observations with attributes including temperature, pH, electrical conductivity, total dissolved solids, water color, odor, and weather condition. The research stages include feature selection, data preprocessing, categorical encoding, Z-score standardization, K-Means clustering, and cluster number evaluation. The number of clusters was tested from K=2 to K=5. Cluster quality was evaluated using Silhouette Score, Davies-Bouldin Index, Calinski-Harabasz Score, and Inertia. After data cleaning, 1,751 observations were used in the clustering process. The evaluation results show that K=2 is the best cluster number, with a Silhouette Score of 0.187638 and a Calinski-Harabasz Score of 456.873808. The clustering results formed two main patterns, namely Cluster 0 with 840 observations or 47.97% and Cluster 1 with 911 observations or 52.03%. Based on average parameter characteristics, Cluster 0 has higher electrical conductivity and TDS values than Cluster 1; therefore, it is interpreted as a higher water quality risk pattern. These results indicate that K-Means can identify initial water quality patterns in an unlabeled Bengawan Solo River dataset.
Comparison of Data Mining Model Performance in Heart Disease Detection with Feature Selection Application: Perbandingan Kinerja Model Data Mining Dalam Deteksi Penyakit Jantung Dengan Penerapan Feature Selection Widya Cholid Wahyudin; Tole Sutikno; Rusydi Umar; Ahmad Ridwan
JOINCS (Journal of Informatics, Network, and Computer Science) Vol. 8 No. 1 (2025): April
Publisher : Universitas Muhammadiyah Sidoarjo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21070/joincs.v8i1.1669

Abstract

Penyakit jantung merupakan penyebab utama kematian di seluruh dunia, sehingga deteksi dini sangat penting untuk meningkatkan harapan hidup pasien. Dengan kemajuan teknologi data mining dan machine learning, prediksi penyakit jantung dapat dilakukan lebih akurat. Penelitian ini membandingkan kinerja prediksi model Logistic Regression, Decision Tree, Random Forest, K-Nearest Neighbors (KNN), dan Support Vector Machine (SVM) dalam mendeteksi penyakit jantung menggunakan UCI Heart Disease Dataset. Teknik feature selection—Filter Method, Wrapper Method (RFE), dan Embedded Method—diterapkan untuk meningkatkan akurasi prediksi dan mengurangi kompleksitas model. Hasil eksperimen menunjukkan bahwa SVM mencapai akurasi tertinggi sebesar 91,2%, diikuti Random Forest dengan 90,7%. Penggunaan feature selection terbukti meningkatkan kinerja model secara signifikan dengan mengurangi dimensi data dan menghindari overfitting. Temuan ini menunjukkan efektivitas SVM dan Random Forest dalam pengembangan sistem prediksi penyakit jantung yang efisien di lingkungan klinis. Kata kunci: Data Mining, Prediksi Penyakit Jantung, Feature Selection, Support Vector Machine
Co-Authors Achmad Ridwan Achmad Ridwan Agung Prihandono Ahmad Ridwan Anton Yudhana Deka Setia Negara Fida Maisa Hana Imam Riyadi Osama Maulana Haq Rusydi Umar Rusydi Umar Saiful Ulya Taftazani Ghazi Pratama Tole Sutikno Tole Sutikno Tole Sutikno Widya Cholid Wahyudin Wilda Rina Hasibuan