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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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IMPLEMENTASI ALGORITMA CART DALAM KLASIFIKASI PENYAKIT DIABETES Fida Maisa Hana; Widya Cholid Wahyudin; Saiful Ulya; Deka Setia Negara
JURNAL ILMU KOMPUTER DAN MATEMATIKA Vol 4, No 1 (2023): JURNAL ILMU KOMPUTER DAN MATEMATIKA
Publisher : Universitas Muhammadiyah Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26751/jikoma.v4i1.1786

Abstract

Diabetes is a metabolic disease caused by a lack of insulin production in the pancreas, this results in an imbalance of sugar in the blood so that the concentration of blood sugar levels increases. Patients with diabetes from year to year are increasing. Estimates from the International Diabetes Federation (IDF), there are 382 million people suffering from diabetes in 2012. It is estimated that by 2035 the number will increase to 592 million people. Recording of this disease needs to be done so that prevention can be done. One of the records that can be done is by utilizing data mining classification techniques. This study implements the CART (Classification And Regression Trees) algorithm in the classification of diabetes. The highest accuracy results were obtained when classification using the CART algorithm without pruning and prepruning was 100%. Meanwhile, pruning and prepruning produce an accuracy of 96.15%.Keywords : data mining, classification, diabetes, CART.
IMPLEMENTASI WEBSITE INTERAKTIF UNTUK SISTEM MANAJEMEN PEMBAGIAN GAJI PERUSAHAAN Osama Maulana Haq; Taftazani Ghazi Pratama; Widya Cholid Wahyudin
JURNAL ILMU KOMPUTER DAN MATEMATIKA Vol 6, No 2 (2025): JURNAL ILMU KOMPUTER DAN MATEMATIKA
Publisher : Universitas Muhammadiyah Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26751/jikoma.v6i2.2908

Abstract

The implementation of a PHP Laravel-based payroll management system has proven to be an effective solution for optimizing company operations in terms of payroll and employee attendance management. With features including attendance data management, automated payroll calculation, deductions, and the ability to generate analytical reports, this system provides high transparency, efficiency, and accuracy. The use of Laravel as the primary framework ensures that the system is secure, flexible, and easily integrable with other modules. The outcomes of this implementation show improvements in strategic decision-making and employee satisfaction, ultimately supporting overall company performance and growth.Keywords: Transparency, Efficiency, and PHP Laravel
PERANCANGAN WEBSITE INTERAKTIF UNTUK MERENCANAKAN PERJALANAN BERBASIS TEKNOLOGI DAN INFORMASI TERKINI Osama Maulana Haq; Widya Cholid Wahyudin; Taftazani Ghazi Pratama; Agung Prihandono
JURNAL ILMU KOMPUTER DAN MATEMATIKA Vol 5, No 2 (2024): JURNAL ILMU KOMPUTER DAN MATEMATIKA
Publisher : Universitas Muhammadiyah Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26751/jikoma.v5i2.2497

Abstract

Pengembangan website Traveluki sebagai solusi inovatif dalam mendukung kebutuhan pengguna dalam  merencanakan  perjalanan.  Dengan  fokus  pada  teknologi  informasi,  artikel  menguraikan  faktor latar  belakang,  seperti  peningkatan  penggunaan  smartphone,  tren  pariwisata  digital,  personalisasi pengalaman  wisata,  dan  pemanfaatan  teknologi  Augmented  Reality  (AR)  dan  Virtual  Reality  (VR). Dukungan  dari  pemerintah  dan  industri  pariwisata,  serta  penekanan  pada  keakuratan  dan  keamanan informasi, menjadi prinsip utama dalam pengembangan website ini. Materi dan metode pengembangan, termasuk penggunaan HTML, CSS, JavaScript, Python, MongoDB, dan Flask, dijelaskan dengan detail. Sitemap  untuk  pengunjung  wisata  dan  pemilik  wisata  disajikan  dengan  rinci,  menggambarkan  rute navigasi yang intuitif. Hasil dan pembahasan mencakup wireframe dari berbagai halaman kunci, seperti Home,  Discover,  Cek  Booking,  Cek  Tiket,  Tentang  Kami,  Register,  dan  Login,  dengan  fokus  pada keamanan,  efisiensi,  dan  ramah  pengguna.  Artikel  ini  berharap  bahwa  Traveluki  dapat  memberikan kontribusi  positif  terhadap  industri  pariwisata  dengan  memanfaatkan  teknologi  dan  informasi  yang akurat, memenuhi ekspektasi pengguna modern. Kata  Kunci:  Teknologi  Informasi,  Keamanan  Informasi,  HTML,  CSS,  JavaScript,  Python,  MongoDB, Flask
OPTIMASI PARAMETER ALGORITMA DECISION TREE C4.5 PADA KLASIFIKASI BLOGGER PROFESSIONAL Fida Maisa Hana; Widya Cholid Wahyudin
JURNAL ILMU KOMPUTER DAN MATEMATIKA Vol 4, No 2 (2023): JURNAL ILMU KOMPUTER DAN MATEMATIKA
Publisher : Universitas Muhammadiyah Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26751/jikoma.v4i2.2141

Abstract

Blogger merupakan salah satu pekerjaan yang paling dicari. Bisnis yang dapat menghasilkan banyak uang jika ditekuni secara mendalam. Di era teknologi komputer yang terus berkembang dengan pesat, kita dapat menghemat waktu untuk mengklasifikasikan bloger profesional atau bukan menggunakan metode data mining. Untuk memprediksi bloger profesional menggunakan data mining, diperlukan elemen pendukung untuk menentukan dan data yang valid. Penelitian ini menggunakan teknik klasifikasi data mining untuk menentukan bloger profesional. Algoritma yang digunakan adalah decision tree C4.5. Penelitian ini Menggunakan 4-Fold Cross Validation dan Optimasi paramter apply prepurning, apply purning, dan minimal leaf size pada algoritma C4.5. Hasil akurasi Pengujian sebesar 88,00 % dengan Precision sebesar 87,30% %, dan Recall sebesar 75,00%.
PREDIKSI STUNTING PADA BALITA DI RUMAH SAKIT KOTA SEMARANG MENGGUNAKAN NAIVE BAYES Widya Cholid Wahyudin; Fida Maisa Hana; Agung Prihandono
JURNAL ILMU KOMPUTER DAN MATEMATIKA Vol 4, No 1 (2023): JURNAL ILMU KOMPUTER DAN MATEMATIKA
Publisher : Universitas Muhammadiyah Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26751/jikoma.v4i1.1792

Abstract

Stunting is chronic malnutrition caused by insufficient nutritional intake over a long period of time due to the provision of food that is not in accordance with needs. This study focuses on malnutrition in toddlers. Stunting in toddlers is more common in toddlers aged 12-59 months than toddlers aged 0-24 months. Stunting can have short and long-term impacts. This study used toddler data for 2018 which was obtained from the Semarang City Health Center with toddlers aged 0-59 months. This research aims to value the classification results of stunting nutritional status in toddlers using the Naive Bayes Classifier algorithm. The Naive Bayes Classifier algorithm is one of the algorithms used for the classification process that can solve problems with large amounts of data so that it can produce a probability value for a hypothesis that is sought. It is proved by the results of testing with the Naive Bayes Classifier algorithm, which was carried out on all data in a dataset of 300 records, the accuracy achieved is 85.33%.
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.
Penguatan Literasi Digital Siswa SMK Mambaul Falah Kudus melalui Edukasi Ancaman Siber, Pinjaman Online, dan Cyber Bullying Achmad Ridwan; Widya Cholid Wahyudin; Wilda Rina Hasibuan; Anton Yudhana; Rusydi Umar
Jurnal Pengabdian Masyarakat Vol. 5 No. 1 (2026): Juni 2026
Publisher : Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/japamas.v5i1.487

Abstract

Penggunaan internet yang tinggi di kalangan siswa SMK Mambaul Falah Kudus berpotensi meningkatkan risiko cybercrime, cyber bullying, dan penyalahgunaan pinjaman online ilegal. Kegiatan Program Pemberdayaan Umat (PRODAMAT) ini bertujuan meningkatkan literasi digital dan keterampilan keamanan akun siswa melalui edukasi berbasis studi kasus dan praktik langsung. Metode kegiatan meliputi koordinasi mitra, observasi kebutuhan, penyusunan materi, pre-test, penyampaian materi, diskusi kasus, praktik pengaturan privasi dan two factor authentication, post-test, serta refleksi. Peserta kegiatan berjumlah 30 siswa kelas XI SMK Mambaul Falah Kudus. Hasil kegiatan menunjukkan peningkatan pemahaman pada aspek  cyber crime dari 55% menjadi 83%, cyber bullying dari 58% menjadi 86%, pinjaman online ilegal dari 50% menjadi 84%, dan keamanan digital dari 56% menjadi 85%. Peserta juga mampu mengenali tautan mencurigakan, membedakan pinjaman online legal dan ilegal, memblokir akun berisiko, serta memahami langkah pelaporan. Kegiatan ini berkontribusi bagi sekolah sebagai model edukasi literasi digital aplikatif untuk membangun perilaku internet yang aman, bijak, dan bertanggung jawab.
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