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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Informatika dan Teknik Elektro Terapan Jurnal Ilmiah KOMPUTASI Sinkron : Jurnal dan Penelitian Teknik Informatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Inotera Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah Jurnal Infomedia MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer IJISTECH (International Journal Of Information System & Technology) JOURNAL OF SCIENCE AND SOCIAL RESEARCH JOURNAL OF INFORMATICS AND COMPUTER SCIENCE Jurnal Sistem Komputer dan Informatika (JSON) Journal of Computer Science, Information Technology and Telecommunication Engineering (JCoSITTE) IJISTECH Rambideun : Jurnal Pengabdian Kepada Masyarakat International Journal of Engineering, Science and Information Technology jeti JURNAL ILMIAH GLOBAL EDUCATION JUSTIN (Jurnal Sistem dan Teknologi Informasi) Jurnal Teknik Informatika dan Elektro (JURTIE) TECHSI - Jurnal Teknik Informatika Sisfo: Jurnal Ilmiah Sistem Informasi Jurnal Teknologi Terapan and Sains 4.0 Jurnal Kemitraan Responsif untuk Aksi Inovatif dan Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Journal of Advanced Computer Knowledge and Algorithms Applied Engineering, Innovation, and Technology Proceedings of International Conference on Multidisciplinary Engineering (ICOMDEN)
Munirul Ula
Universitas Malikussaleh
Agriculture, Biological Sciences & Forestry Humanities Astronomy Automotive Engineering Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Physics Public Health Social Sciences Transportation Other

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Analysis of Customer Churn Classification for Sinarmas Syariah Lhokseumawe Insurance Services Using Deep Learning Tabnet and Explainable AI Syarifah Muliana; Taufiq Taufiq; Munirul Ula
Journal of Applied Informatics and Computing Vol. 10 No. 3 (2026): June 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i3.12864

Abstract

Customer churn is a major challenge in the insurance industry because it directly affects customer retention, business sustainability, and company profitability. Early identification of customers at risk of churn is therefore essential for developing effective retention strategies. This study proposes an interpretable customer churn prediction framework for Sinarmas Syariah Lhokseumawe Insurance Services by integrating TabNet deep learning with Shapley Additive Explanations (SHAP). The dataset consists of 2,000 customer records containing demographic information, insurance transactions, premium payments, claims history, and customer interaction data. Due to the imbalanced class distribution, the Synthetic Minority Oversampling Technique (SMOTE) was applied exclusively to the training dataset to improve model learning while preventing data leakage. Model performance was evaluated using Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The experimental results demonstrate that the proposed approach achieved an accuracy of 98.77%, precision of 86.67%, recall of 92.86%, F1-score of 89.66%, and ROC-AUC of 0.995, indicating excellent classification performance. Furthermore, SHAP analysis revealed that communication, premi_2025, and reason_to_purchase were the most influential features affecting churn predictions. These findings highlight the importance of customer engagement, premium management, and purchasing motivations in customer retention. The proposed TabNet-SHAP framework provides both high predictive performance and model interpretability, making it a valuable decision-support tool for customer retention strategies in the insurance sector.
Classification of Hospital Stay Duration for Schizophrenia Patients at RSUD Muyang Kute Using a Combination of C4.5 and Particle Swarm Optimization Putri Agustina Dewi; Munirul Ula; Said Fadlan Anshari
Journal of Advanced Computer Knowledge and Algorithms Vol. 3 No. 2 (2026): Journal of Advanced Computer Knowledge and Algorithms - April 2026
Publisher : Department of Informatics, Universitas Malikussaleh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29103/jacka.v3i2.25930

Abstract

Schizophrenia is a chronic mental disorder that often requires inpatient care, so an increase in the number of patients can lead to limited bed capacity in psychiatric wards. This study aims to classify the length of hospital stay for schizophrenia patients to support room requirement planning at RSUD Muyang Kute using the C4.5 algorithm optimized with Particle Swarm Optimization (PSO). The dataset consists of 657 medical records of inpatient schizophrenia cases from February 2023 to March 2025, categorized into three length-of-stay classes: short (1–5 days), medium (6–10 days), and long (>10 days). The C4.5 algorithm is used to construct a decision tree model based on historical data, while PSO is employed as an optimization method to improve the model configuration. The evaluation uses classification accuracy and Mean Absolute Percentage Error (MAPE) for room demand estimation. The results show that both the C4.5 and C4.5–PSO models achieve similarly high accuracy on the test data, while the manual MAPE calculation for room demand estimation yields a value of 52.66%. In contrast, the MAPE calculated by the system is 0.00% in the test scenario because all classes in the test data are correctly predicted. The web-based decision support system developed using Python and Streamlit is able to automatically provide predictions of length of stay and estimates of the required number of psychiatric beds at RSUD Muyang Kute.
Implementation of a Hybrid Model Using Principal Component Analysis, K-Means, and Naïve Bayes for Tuition Fee Category Prediction Nurdin Nurdin; Jessika Jessika; Munirul Ula
Journal of Applied Informatics and Computing Vol. 10 No. 4 (2026): August 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i4.13184

Abstract

The determination of Tuition Fee Categories in higher education institutions is commonly conducted through manual verification of students’ socioeconomic documents, which may lead to subjectivity and inconsistencies in decision-making. This study proposes a hybrid machine learning approach that integrates Principal Component Analysis (PCA), K-Means Clustering, and Naïve Bayes Classifier within a semi-supervised learning framework for student socioeconomic classification based on pseudo-labels generated from clustering results. The dataset used in this study consists of 452 student records with 12 socioeconomic attributes obtained from the New Student Admission system of STAIN Teungku Dirundeng Meulaboh in 2025. Data preprocessing includes attribute selection, categorical transformation using One Hot Encoding, and feature standardization. PCA is applied to reduce dimensionality from 18 features to 12 principal components while retaining 95% of the total variance. The processed data are clustered using K-Means with the optimal number of clusters determined as 8 based on Elbow and Silhouette Score analysis. These clusters are used as pseudo-labels for training the Naïve Bayes classifier. Experimental results show that the proposed model achieves 98.89% training accuracy and 97.80% testing accuracy, with a weighted average F1-score of 0.98. The results indicate that the proposed hybrid approach is effective in capturing underlying socioeconomic patterns and provides a stable classification performance. However, the model is based on pseudo-labels rather than official tuition fee categories. Therefore, further validation using real labeled data is recommended to enhance generalizability and practical applicability.
PERANCANGAN VIRTUAL REALITY TOUR GEDUNG-GEDUNG FAKULTAS DI KAMPUS BUKIT INDAH UNIVERSITAS MALIKUSSALEH BERBASIS WEBSITE Affan Syafiq Azzikri; ⁠Dahlan Abdullah; Munirul Ula
JOURNAL OF SCIENCE AND SOCIAL RESEARCH Vol. 8 No. 3 (2025): August 2025
Publisher : Smart Education

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54314/jssr.v8i3.3995

Abstract

Kemajuan teknologi informasi mendorong transformasi di berbagai sektor, termasuk pendidikan tinggi. Salah satu inovasi yang berkembang adalah pemanfaatan Virtual Reality (VR) untuk menciptakan pengalaman eksplorasi kampus secara digital. Penelitian ini bertujuan merancang dan mengembangkan aplikasi Virtual Reality Tour berbasis website untuk menampilkan visualisasi gedung-gedung fakultas di Kampus Bukit Indah, Universitas Malikussaleh. Aplikasi ini memanfaatkan 3DVista untuk panorama 360°, serta framework Laravel dengan HTML, CSS, dan JavaScript untuk membangun antarmuka yang responsif. Pengembangan dilakukan menggunakan metode Multimedia Development Life Cycle (MDLC) yang terdiri dari enam tahap. Evaluasi sistem melalui Blackbox Testing dan User Acceptance Testing (UAT) menunjukkan bahwa aplikasi berfungsi baik, mudah digunakan, dan menyenangkan. Hasil ini mendukung konsep smart campus serta berpotensi sebagai media promosi dan orientasi digital yang efektif.
Co-Authors Abdullah, ⁠Dahlan Affan Syafiq Azzikri Afif, Muhammad Athallah Afridah, Rita Agustriya, Manda Al-Ghiyats, Said Ananda Faridhatul Ulva Andreansyah, Sabda Ar Razi Arnawan Hasibuan Azzikri, Affan Syafiq ⁠Dahlan Abdullah Bustami Bustami Bustami Bustami Cut Agusniar Dahlan Abdullah Dara Farhiyah Dhani, Saniah Dinda, Dinda Fadillah, Rizky Fahruddin Fahruddin Fajriana, F Fajriana, Fajriana Fasdarsyah Fasdarsyah Fidyatun Nisa Fikhri, Aditya Aziz Fitri, Anisa Amelia Fuddin, Mudhya Hamdhana, Defry Hasan Dalimunthe, Amir Husaini Jessika Jessika Kamaruzzaman, Hilda Zulfira KURNIAWATI - Kurniawati Kurniawati Lailatul Husna Lidya Rosnita Lubis, Syahrul Andika M David Khalid Mey Suci Br Pardosi Muhammad Daud Muhammad Fauzan Muhammad Fikry Muhammad Ikhwanus Muhammad Muhammad Muhammad Yani, Muhammad Mutammimul Ula Muthalib, Muchlis Abd Nadia Saphira Nanda Imanda Nasution, Wahidatunnisa Nurdin Nurdin Nurdin Nurdin Nurdin Nurul Aula Nurul Husna Putri Agustina Dewi Putri, Nazirah Allisya Rahman, Ashri Nurhajizah Ridha, Ridha Rini Meiyanti Rizal Rizal Rizal S.Si., M.IT, Rizal Rizal Tjut Adek Rizki Suwanda Rizky Putra Fhonna Rizky, Rahmat Rozzi Kesuma Dinata Rusadi, Athirah Said Fadlan Anshari Saiful Kiram Salimuddin, Salimuddin Sayed Fachrurrazi Sayed Fachrurrazi Sayuti, Muhammad Siagian, Tania Annisa Sinambela, Reza Syahputra Siska Amelia Melani Siti Aminah Sudarti, Atrida Sujacka Retno Susanti Susanti Syarifah Muliana Taufiq Taufiq Taufiq Taufiq Tiara Oktavia Ulfah, Julia Veri Ilhadi Yasin, Fijri Ahmad Yessi Apprilia Yesy Aflillia Yesy Afrillia Yopy Anfelia Yulisda, Desvina Yuni SariBr Sitepu Zailani Mohamed Sidek Zara Yunizar