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All Journal Techno.Com: Jurnal Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Technology and Computer Science Knowledge Engineering and Data Science InComTech: Jurnal Telekomunikasi dan Komputer JOURNAL OF APPLIED INFORMATICS AND COMPUTING TEKTRIKA - Jurnal Penelitian dan Pengembangan Telekomunikasi, Kendali, Komputer, Elektrik, dan Elektronika JOISIE (Journal Of Information Systems And Informatics Engineering) JISKa (Jurnal Informatika Sunan Kalijaga) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Community Development Journal: Jurnal Pengabdian Masyarakat Jurnal Teknologi Informatika dan Komputer Jurnal Teknik Informatika (JUTIF) Jurnal Restikom : Riset Teknik Informatika dan Komputer JINAV: Journal of Information and Visualization Jurnal Pendidikan dan Teknologi Indonesia Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Pengabdian Masyarakat Bhinneka Journal of Training and Community Service Adpertisi Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Digital Business Jurnal Penelitian Sistem Informasi Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Journal of Enhanced Studies in Informatics and Computer Applications J-KOMA : Jurnal Ilmu Komputer dan Aplikasi International Journal of Computer Science and Information Technology Jati Emas (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) Journal of World Future Medicine, Health and Nursing
Mochammad Anshori
Institut Teknologi, Sains, Dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Other

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Komparasi Distance Measure pada K-Means dalam Klasterisasi Peserta KB Aktif Mochammad Anshori; Afifah Vera Ferencia Fitria Ningrum; Risqy Siwi Pradini
JISKA (Jurnal Informatika Sunan Kalijaga) Vol. 11 No. 1 (2026): January 2026
Publisher : UIN Sunan Kalijaga Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14421/jiska.5006

Abstract

The rapid population growth in Indonesia poses significant challenges to public welfare, economic stability, and sustainable development. The Family Planning program aims to regulate population growth through various contraceptive methods; however, participation rates often differ across regions. Understanding these variations is crucial for designing targeted interventions. This study investigates how different distance measures in the K-Means clustering algorithm affect the segmentation quality of KB participants in Kalirejo Village, Lawang District. Eight distance metrics—Euclidean, Manhattan, Minkowski, Chebyshev, Mahalanobis, Bray-Curtis, Canberra, and Cosine—were compared using standardized data from the local BKKBN office (January–September). Cluster validity was evaluated using the Silhouette Coefficient across k=2–10. Results show that the Manhattan distance with k=2 achieved the best clustering quality (SC = 0.7191), effectively distinguishing participant groups by contraceptive method preference. The study highlights the importance of selecting suitable distance measures to improve data-driven policy and decision-making in family planning management.
Predicting Heart Disease using Logistic Regression Anshori, Mochammad; Haris, M. Syauqi
Knowledge Engineering and Data Science
Publisher : citeus

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

A common risk of death is caused by heart disease. It is critical in the field of medicine to be able to diagnose cardiac disease in order to adequately prevent and treat patients. The most accurate method of prediction has the potential to both extend the patient's life and reduce the severity of their cardiac disease. The use of machine learning is one approach that may be taken to generate predictions. In this study, patient medical record information was used in conjunction with an algorithm for logistic regression in order to make heart disease diagnoses. The outcomes of the logistic regression have been utilized to achieve a high level of accuracy in the prediction of heart disease. To get the model coefficients needed for the equation, the experiment uses an iterative form of the logistic regression test. Iteration 14 produced the best results, with an accuracy of 81.3495% and an average calculation time of 0.020 seconds. The best iteration was reached at that point. The percentage of space that lies beneath the ROC curve is 89.36%. The findings of this study have significant implications for the field of heart disease prediction and can contribute to improved patient care and outcomes. Accurate predictions obtained through logistic regression can guide healthcare professionals in identifying individuals at risk and implementing preventive measures or tailored treatment plans. The computational efficiency of the model further enhances its applicability in real-time decision support systems.
Enhancing Cyberbullying Sentiment Detection: A Comparative Study of IndoBERT and IndoBERTweet over SMOTE and Bernoulli Naive Bayes Approach Eka Mardiana Putri; Mochammad Anshori; M. Syauqi Haris
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.v10i3.12632

Abstract

Cyberbullying has become a critical issue in social media use because it can negatively impact users’ mental health and social interactions. The high volume of aggressive comments and hate speech on digital platforms highlights the need for an automatic detection system that can accurately and reliably identify cyberbullying content. This research compares the performance of Indonesian language transformer models, IndoBERT and IndoBERTweet, in detecting text-based cyberbullying. Before modeling, the dataset undergoes Exploratory Data Analysis to understand its characteristics, class distribution, comment length, and potential data imbalance. Next, text preprocessing and tokenization are performed before dividing the data using stratified holdout splitting to preserve class proportions in training and testing sets. Both models are then trained with the same hyperparameter settings to ensure an objective and fair performance comparison. Results show that IndoBERT achieved an accuracy of 0.8333, while IndoBERTweet performed better with an accuracy of 0.8409. The analysis of the confusion matrix and ROC curve confirms that IndoBERTweet is more effective at detecting cyberbullying across different classes. Compared to previous studies using the SMOTE method and Bernoulli Naïve Bayes algorithm, which achieved 84.00% accuracy, this study's findings are slightly higher at 84.09%. Notably, this was achieved without using synthetic oversampling techniques. This suggests that the approach employed in this research can deliver competitive performance even without data balancing with SMOTE. Overall, these findings indicate that a transformer-based approach, combined with a more representative dataset, can improve cyberbullying detection more efficiently and practically. Therefore, IndoBERTweet is a more suitable model for implementing a cyberbullying content moderation system in Indonesia.
Classification of the Effectiveness of Balur Therapy on Patients at the Malang Health Center Using the Decision Tree Algorithm Riski Puji Lestari; Mochammad Anshori; Wahyu Teja Kusuma
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.v10i3.12730

Abstract

This study addresses the classification of balur therapy effectiveness as a complementary treatment using a machine learning approach, aiming to develop an accurate, balanced, and transparent model to support clinical decision-making. The methodology employs the Decision Tree algorithm, data imbalance handling using Synthetic Minority Oversampling Technique, and model interpretation through Local Interpretable Model-Agnostic Explanations. The dataset consists of 520 medical records, reduced to 478 after preprocessing, including data cleaning, binning, and outlier removal. The results indicate that the model without data balancing achieved the highest specificity of 0.8276 at a 90:10 split ratio, while the application of Synthetic Minority Oversampling Technique improved sensitivity toward the minority class but reduced specificity. Key influential features include occupation, diagnosis, and therapy duration. The interpretability analysis demonstrates that the model can clearly explain feature contributions to predictions. This study concludes that integrating classification, data balancing, and explainable modeling enhances medical data analysis. The findings imply strong potential for developing objective and transparent clinical decision support systems.
Identifying Fear of Missing Out (FOMO) in Adolescents Using K-Nearest Neighbors: An Experimental Study of k-Values and Distance Metrics Ricco Wahyu Pamungkas; Mochammad Anshori; Wahyu Teja Kusuma
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.v10i3.12879

Abstract

Fear of Missing Out (FOMO) is a psychological phenomenon commonly experienced by teenagers due to the high intensity of social media use, and has the potential to cause emotional and social impacts if not identified early. The main problem in identifying FOMO is its internal nature and the difficulty in measuring it objectively using conventional methods. This research proposes a data mining-based classification approach using K-Nearest Neighbor (KNN) to identify the level of FOMO in adolescents. The dataset was obtained from 136 respondents through a questionnaire that included demographic data and the ON-FoMO scale. The research stages include data preprocessing (encoding and Min-Max normalization), data splitting using stratified holdout (80:20), and experiments varying K (3–19) and distance metrics (Euclidean, Manhattan, Chebyshev). The experimental results show that the combination of Euclidean distance with K=11 yields the best performance with an accuracy of 85.71%, ROC AUC of 0.786, Precision–Recall AUC of 0.826, and sensitivity of 100%. The experimental results indicate that the selection of the K parameter and the distance method significantly affect classification performance. Overall, this study concludes that the KNN algorithm with the optimal configuration is effective as an initial screening method for the level of FOMO in adolescents in an systematic and data-based manner.
Evaluasi Usability pada Aplikasi Identitas Kependudukan Digital (IKD) Menggunakan System Usability Testing Faridatus Sholikhah; Mochammad Anshori; M. Syauqi Haris
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 3 (2026): Agustus : JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i3.4473

Abstract

This study aims to evaluate the usability of the Digital Population Identity (Identitas Kependudukan Digital/IKD) application using the System Usability Scale (SUS) method. A descriptive quantitative approach was employed, with the study conducted at the Department of Population and Civil Registration (Disdukcapil) of Malang City. Data were collected through a Google Forms-based SUS questionnaire distributed to 50 respondents selected using purposive sampling, consisting of users with prior experience using the IKD application. The research instrument comprised 10 SUS statements measured on a five-point Likert scale. The validity test indicated that all questionnaire items were valid with r-values exceeding 0.2732, while the reliability test produced a Cronbach's Alpha value of 0.834, indicating good internal consistency. The SUS evaluation yielded an average score of 77.42, categorized as Acceptable, achieving Grade B with a Good adjective rating. However, the Net Promoter Score (NPS) remained in the Passive category, indicating the need for improvements in data integration stability and facial registration procedures to enhance user satisfaction and encourage stronger user advocacy.
Analisis Usability pada SIAPEL-TEGAS dalam Pengurusan KTP-el dengan Menggunakan Metode System Usability Scale (SUS) vita mei fudnia watiningsih; Mochammad Anshori; Ahsanun Naseh Khudori
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 4 No. 3 (2026): Agustus : JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v4i3.4523

Abstract

SIAPEL-TEGAS (Integrated Population Administration Information System) is a digital service provided by the Department of Population and Civil Registration of Malang City that enables citizens to process population administration documents, including electronic identity cards (e-KTP), online. This study aims to analyze the usability level of the SIAPEL-TEGAS application in e-KTP processing services using the System Usability Scale (SUS) method. Data were collected through an online questionnaire distributed to 50 respondents who had previously used the service, employing a purposive sampling technique. The questionnaire consisted of 10 standard SUS statements measured using a 5-point Likert scale. The results showed that the SIAPEL-TEGAS application achieved an average SUS score of 85.35, which falls into the "Acceptable" category with a Grade A rating and an "Excellent" adjective rating. The validity test of all questionnaire items indicated that the calculated correlation coefficients (r-count) were higher than the critical value of r-table (0.2732), confirming that all items were valid. Meanwhile, the reliability test using Cronbach’s Alpha produced a value of 0.738, which is categorized as reliable (acceptable). These findings indicate that the SIAPEL-TEGAS application generally demonstrates an excellent level of usability in terms of effectiveness, efficiency, and user satisfaction. However, several aspects still require improvement to further optimize the user experience.
Prediction Model for Diagnosing Heart Disease Using Classification Algorithm Risqy Siwi Pradini; Mochammad Anshori; M. Syauqi Haris; Busatto Marilia; Tostes Geraldo
Journal of World Future Medicine, Health and Nursing Vol. 1 No. 2 (2023)
Publisher : Yayasan Adra Karima Hubbi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55849/health.v1i2.347

Abstract

Heart disease often causes death if not treated quickly and appropriately. Early diagnosis can prevent more serious complications and treat heart disease patients best. The existence of a disease prediction model can help health workers to diagnose diseases more quickly and accurately. The heart disease prediction model using a classification algorithm is a system built using machine learning techniques. The classification algorithm chosen is NN, Naive Bayes, Random Forest, and SVM because it is the best algorithm for predicting heart disease. This study makes a comparison of the four algorithms using a dataset of 918 instances with 11 features. The result is that the Random Forest algorithm produces the highest accuracy, with 86.8%, and has the best ability to distinguish classes based on the ROC curve.
Co-Authors ACHMAD JAELANI RUSDI Afifah Vera Ferencia Fitria Ningrum Ahmad Afif Supianto Ahsanun Naseh Khudori Ali Hasan, Hildan Adisqi Anis Ansyori Ardimansyah Ardimansyah Arif Wahyudi Busatto Marilia Choirunnisa, Rosyida Danang Bagus Baskoro Dio Wahyu Saputra Dita Kurnia Rachmasari Dyah Utami, Kartika Eka Mardiana Putri Faridatus Sholikhah Fita Rusdian Ikawati Geraldo, Tostes Heri Wahyu Wibowo Herman Tolle Herry Prasetyo Wibowo Herry Wahyu Wibowo Irnanda Septian Ika Putri Javier Fajri Zachary Jimmy H Moedjahedy Khofida, Nisa M. Syauqi Haris Marilia, Busatto Mentari Putri Jati Mufid Musthofa Muhammad Hasbi Musthofa, Mufid Naufal Alif Vivaldi Nindynar Rikatsih Nindynar Rikatsih Ningrum, Afifah Vera Ferencia Fitria Novelia Mega Puspita Nugroho Teguh Yuono Prasetiyo, Bagus Dadang Putri, Irnanda Septian Ika Putrinugroho, Ivana Aikozora R. A. Rengganis Ularan Ricco Wahyu Pamungkas Riski Puji Lestari Risqy Siwi Pradini Risqy Siwi Pradini Risqy Siwi Pradini Saletia, Maria Claudia Samuel PD Anantadjaya Saputra, Dio Wahyu Susi Indriyani Tostes Geraldo vita mei fudnia watiningsih Wahyu Teja Kusuma Wahyu Teja Kusuma Wahyu Tejakusuma, Wahyu Wayan Firdaus Mahmudy Wibowo, Heri Wahyu Yahya, Furqon Nurbaril Yuono, Nugroho Teguh