Article Per Year (5 Year)
p-Index From 2021 - 2026
5.349
P-Index
This Author published in this journals
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 Journal of World Future Medicine, Health and Nursing Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Journal of Enhanced Studies in Informatics and Computer Applications International Journal of Computer Science and Information Technology
Mochamad 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 Medicine & Pharmacology Nursing Public Health Veterinary Other

Published : 40 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 40 Documents
Search

 1   2   3   4 
Comparing Discriminant Analysis Function for Early Prediction of Smartphone Addiction Mufid Musthofa; Mochammad Anshori
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.12

Abstract

The pervasive use of smartphones in daily life has led to significant benefits, but excessive use has caused alarming behavioral and health issues, particularly among adolescents. Addressing smartphone addiction requires early detection to enable timely interventions. This study investigates the application of machine learning, specifically Linear Discriminant Analysis (LDA) and Quadratic Discriminant Analysis (QDA), for the early prediction of smartphone addiction. The research used a dataset containing 394 instances categorized into "addicted" and "non-addicted" classes. Dataset is derived from questionnaire responses. After preprocessing steps, including feature selection and ordinal encoding, the data was split using 10-fold cross-validation to ensure robust evaluation. The models were assessed using metrics such as accuracy, precision, recall, and F-measure. Results indicate that LDA significantly outperforms QDA across all metrics, achieving an accuracy of 94.16%, a precision of 94.2%, a recall of 94.2%, and an F-measure of 94.2%. Additionally, the Receiver Operating Characteristic (ROC) curve analysis showed an Area Under the Curve (AUC) of 0.9875 for LDA, indicating its high reliability and stability in classifying smartphone addiction. QDA, while effective, has a slightly lower performance due to the linear separability of the dataset. This study concludes that LDA is a robust and effective method for early prediction of smartphone addiction, offering valuable insights for health monitoring systems. The findings provide a foundation for future applications of discriminant analysis in addressing behavioral health issues.
MediStock: Medical Stock Website Development Using Design Thinking Novelia Mega Puspita; Dita Kurnia Rachmasari; Naufal Alif Vivaldi; Mochammad Anshori
Journal of Enhanced Studies in Informatics and Computer Applications Vol. 2 No. 1 (2025): JESICA Vol. 2 No. 1 2025
Publisher : Institut Teknologi, Sains, dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47794/jesica.v2i1.25

Abstract

Pharmacies play a vital role in public health by ensuring the availability of essential medications. However, inefficient inventory management systems, particularly in Malang, lead to operational inefficiencies, stock discrepancies, and regulatory compliance challenges. This study aims to develop a web-based inventory management system, MediStock, using the Design Thinking methodology to address these issues effectively. The research employs a human-centered approach, focusing on user needs and experiences through the stages of empathize, define, ideate, prototype, and testing. The system integrates real-time stock monitoring, predictive analytics, and compliance with electronic medical records, enhancing operational efficiency and regulatory adherence. Results indicate that MediStock significantly improves inventory management by minimizing stock discrepancies, optimizing procurement processes, and ensuring real-time visibility of medicine stocks. The heuristic evaluation revealed high usability and adaptability among different user groups, confirming the system's effectiveness and the user-centered design. These findings highlight the potential of Design Thinking to bridge the gap between complex technological solutions and user needs in healthcare inventory management. This study contributes to the field by providing an innovative, user-friendly inventory management solution that enhances operational efficiency and regulatory compliance. Future research should explore the scalability of the system and its integration with broader healthcare management systems to maximize its impact on the healthcare sector.
SECURITY ANALYSIS OF COLLEGE WEBSITES USING VULNERABILITY ASSESSMENT METHOD Dio Wahyu Saputra; Risqy Siwi Pradini; Mochammad Anshori
International Journal of Computer Science and Information Technology Vol. 2 No. 1 (2025): IJCOMIT Vol 2 No 1
Publisher : Computer Science Department, Malang National Institute of Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/ijcomit.v2i1.13206

Abstract

Web application security, especially on college websites, is a critical aspect in maintaining data integrity and protecting users from cyber threats. This research evaluates the security of college websites using the vulnerability assessment method. By utilizing tools such as OWASP ZAP and penetration testing techniques, this research identifies weaknesses at various security layers, including vulnerabilities to Path Traversal attacks, SQL Injection, and deficiencies in the implementation of HTTP security header settings. The analysis shows that the main vulnerabilities are caused by a lack of input validation, inadequate security configuration, and the use of outdated software libraries. This research provides practical solutions such as strengthening security configurations, system updates, and implementing modern security policies to mitigate risks. These findings aim to enhance the security of college websites and create a safer online environment.
PERANCANGAN VIRTUAL REALITY RESEARCH EXPO MENGGUNAKAN DESIGN SPRINT UNTUK MENINGKATKAN KETERLIBATAN PENGGUNA Baskoro, Danang Bagus; Kusuma, Wahyu Teja; Anshori, Mochammad
JOISIE (Journal Of Information Systems And Informatics Engineering) Vol 9 No 1 (2025)
Publisher : Institut Bisnis dan Teknologi Pelita Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35145/joisie.v9i1.4896

Abstract

Pameran penelitian (Research Expo) merupakan sarana penting dalam mendiseminasikan hasil riset kepada publik. Namun, keterbatasan ruang fisik, biaya penyelenggaraan, dan rendahnya partisipasi menjadi tantangan yang sering dihadapi. Penelitian ini mengusulkan solusi berbasis teknologi dengan merancang Virtual Reality Research Expo menggunakan metode Design Sprint, yang dikombinasikan dengan pendekatan persona, teknik Crazy 8’s, dan strategi Minimum Viable Product (MVP). Tujuan penelitian ini adalah menghasilkan prototipe pameran virtual yang interaktif dan sesuai dengan kebutuhan pengguna. Rancangan prototipe dikembangkan melalui enam tahapan Design Sprint, mulai dari penggalian kebutuhan hingga validasi. Persona disusun berdasarkan hasil survei awal, dan ide desain dieksplorasi menggunakan teknik Crazy 8’s. MVP dikembangkan menggunakan A-frame dan Blender 3D, dan diuji kepada lima partisipan yang memiliki pengalaman mengikuti pameran riset. Evaluasi dilakukan melalui kuesioner skala Likert, checklist pencapaian persona goals, dan wawancara semi-terstruktur. Hasil validasi menunjukkan bahwa 6 dari 7 persona goals terpenuhi, dengan skor rata-rata 4,4 dari 5 untuk kemudahan navigasi, 4,6 untuk visualisasi, dan 4,2 untuk interaktivitas. Partisipan menyatakan bahwa prototipe memberikan pengalaman eksplorasi yang lebih menarik dibandingkan pameran konvensional. Kebaruan dari penelitian ini terletak pada integrasi metode desain terstruktur dengan validasi berbasis kebutuhan pengguna dalam konteks VR untuk pameran akademik, yang masih jarang diterapkan di lingkungan pendidikan tinggi di Indonesia. Temuan ini menunjukkan bahwa pendekatan iteratif berbasis Design Sprint efektif
Prediction of Sleep Disorder: Insomnia Using AdaBoost Ensemble Learning Algorithm with Grid Search Optimization Anshori, Mochammad; Kusuma, Wahyu Teja; Pradini, Risqy Siwi
InComTech : Jurnal Telekomunikasi dan Komputer Vol 14, No 1 (2024)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v14i1.19306

Abstract

Human health is an important thing to keep. Health has to be maintained with appropriate rest. Lack of rest has a bad impact on the body such as hormonal imbalances. One of the causes of lack of rest is insomnia. Insomnia is a phenomenon that describes someone's difficulty sleeping. Insomnia is often considered trivial, but chronic insomnia puts the sufferer at risk of serious illness physically and psychologically. Some people sometimes don't realize that they have insomnia because they feel like they have trouble sleeping. Therefore, early detection of insomnia is necessary to do. This study uses a machine learning approach to make predictions, namely the AdaBoost + grid search method. AdaBoost is used because of its reliability in making strong classifiers and grid search is applied to tuning parameters from AdaBoost. Parameters that are optimized are the n estimator and learning rate. Parameter tuning by grid search gives n – estimator = 76 and learning rate = 0.1. Some preprocess technique is done, there are normalization and ordinal encoding then data splitting based on the determined ratio. There are 80% for training data and 20% for testing data. On training data, the result is 98% percentage for each accuracy, precision, recall, and f1 score. This value is better than the comparison method, it is LogRegression that only reaches 97% value on each evaluation measure. The model implemented on test data and AdaBoost + grid search obtained 100% accuracy, precision, recall, and f1 score. However, LogRegression only gives 98% result. This study proved that AdaBoost with grid search is sustainable to do early prediction of insomnia.
PENINGKATAN KOMPETENSI PELAKU UMKM KOTA BATU DALAM BRAND AWARENESS MELALUI PELATIHAN BERBASIS ARTIFICIAL INTELLIGENCE Pradini, Risqy Siwi; Haris, M. Syauqi; Anshori, Mochammad
Community Development Journal : Jurnal Pengabdian Masyarakat Vol. 6 No. 3 (2025): Volume 6 No 3 Tahun 2025
Publisher : Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/cdj.v6i3.44159

Abstract

Program Pengabdian kepada Masyarakat ini bertujuan untuk meningkatkan kompetensi pelaku UMKM di Kota Batu dalam memperkuat brand awareness produk melalui pelatihan berbasis Artificial Intelligence. Pelatihan ini dilakukan dengan menggunakan metode Participatory Learning and Action yang mengedepankan interaksi aktif dan praktik langsung dari para peserta pelatihan. Materi pelatihan mencakup konsep dasar branding, pemanfaatan media sosial, serta aplikasi teknologi AI seperti Canva dan ChatGPT dalam pembuatan desain visual untuk meningkatkan brand awareness produk UMKM. Evaluasi yang dilakukan melalui pre-test dan post-test menunjukkan peningkatan signifikan rata-rata pemahaman peserta, dari skor awal 63 menjadi 90,33. Hasil ini membuktikan bahwa metode pelatihan yang digunakan efektif dalam meningkatkan kompetensi para peserta. Pelatihan ini juga mendorong peserta untuk lebih percaya diri dalam memanfaatkan teknologi untuk pemasaran digital, sehingga mampu bersaing di pasar yang semakin kompetitif.
Improving Random Forest Evaluation in Mental Health Disorder Identification with Cross Validation Choirunnisa, Rosyida; Anshori, Mochammad; Kusuma, Wahyu Teja
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.1053

Abstract

Mental health disorders are often difficult to detect and diagnose, causing misdiagnoses which lead to inappropriate treatment and have a negative impact on the sufferer's quality of life. This research aims to develop an accurate and efficient model for identifying mental health disorders by utilizing the Random Forest method and Cross Validation techniques. Random Forest was chosen because of its ability to improve prediction accuracy and training speed. Cross Validation is used to train and test models with various combinations of data, and reduces the risk of Overfitting. The dataset consists of 120 data with 18 behavioral attributes and diagnoses, with four target classes: Bipolar Type-1, Bipolar Type-2, Depression, and Normal. Four Cross Validation experimental scenarios were tested: k=5 and k=10, and k=5 and k=10 with Stratified to reduce data bias. Experimental results show that k=10 stratified cross-validation produces the highest accuracy (87.5%), with precision, recall, and F1-score also reaching 87.5%. The Stratified technique is proven to improve the balance of class distribution and reduce the risk of Overfitting. These findings confirm that Random Forest with k=10 Stratified Cross-Validation is the optimal approach for diagnosing mental health disorders. The implications of this research include the potential for applying models in AI-based systems to assist medical personnel in more accurate and efficient early diagnosis.
Linkage Comparison in Agglomerative Hierarchical Clustering for Clustering Students' Knowledge of First Aid for Stroke Emergencies Putrinugroho, Ivana Aikozora; Anshori, Mochammad; Kusuma, Wahyu Teja
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 2 (2025): Mei - Juli
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i2.564

Abstract

Stroke is a leading cause of disability and mortality worldwide, necessitating immediate and accurate first aid to mitigate severe outcomes. In Indonesia, limited public knowledge about stroke management, particularly among high school students, underscores the urgent need for targeted educational interventions. This study aims to evaluate students’ understanding of stroke first aid and identify optimal methods for clustering educational data using Agglomerative Hierarchical Clustering (AHC). A validated questionnaire was distributed to 112 high school students, focusing on their knowledge of stroke symptoms, risk factors, and first-aid practices. Data preprocessing ensured quality and consistency before applying AHC with three linkage methods: Single Linkage, Complete Linkage, and Ward’s method. The results were evaluated using Davies-Bouldin Index and Silhouette Coefficient to determine the most effective clustering approach. Ward’s method outperformed other linkage methods, achieving superior cluster compactness and separation. Four clusters were identified, representing varying levels of knowledge, from basic understanding to high awareness of stroke and seizure management. These findings provide a foundation for designing tailored educational programs, addressing specific knowledge gaps, and enhancing firstaid preparedness. This study demonstrates the utility of machine learning in educational research and contributes to improving public health education. Future research should expand on these findings by incorporating diverse datasets and alternative clustering algorithms.
SMOTE Effectiveness and various Machine Learning Algorithms to Predict Self-Esteem Levels of Indonesian Student Anshori, Mochammad; Siwi Pradini, Risqy; Teja Kusuma, Wahyu
Engineering, MAthematics and Computer Science Journal (EMACS) Vol. 7 No. 2 (2025): EMACS
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/emacsjournal.v7i2.13521

Abstract

Self-esteem plays a crucial role in students' psychological well-being, influencing their academic performance and personal development. Despite its importance, self-esteem is challenging to measure due to its abstract and subjective nature. This study aims to develop a predictive model to classify students’ self-esteem levels as high or low using machine learning and tabular data obtained through questionnaires. A dataset comprising 47 student responses, with 19 features consisting of social, emotional, demographic aspects, were analyzed. Five machine learning models were evaluated: Naïve Bayes, Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine (SVM). To address the class imbalance in the dataset, the study applied SMOTE for data balancing and min-max normalization for feature standardization. Model performance was assessed using accuracy and F1-score. The results reveal that SVM, particularly with an RBF kernel, outperformed other models across all scenarios. On raw data, SVM achieved 66% accuracy and an F1-score of 57.3%. After applying SMOTE, the performance improved to 80% accuracy and a 79.9% F1-score. Further enhancement with normalization resulted in the best performance, with SVM achieving 83.33% accuracy and an F1-score of 83.3%. These results demonstrate how well preprocessing methods work to enhance machine learning models for datasets that are unbalanced. The proposed SVM-based model offers promising applications in educational and psychological settings, enabling early interventions to support students’ mental health.
Klasifikasi Spesies Jamur Beracun Agaricus Xanthodermus dan Amanita Muscaria Menggunakan Transfer Learning dengan Arsitektur MobileNetV2 Ali Hasan, Hildan Adisqi; Siwi Pradini, Risqy; Anshori, Mochammad
Jurnal Pendidikan dan Teknologi Indonesia Vol 5 No 9 (2025): JPTI - September 2025
Publisher : CV Infinite Corporation

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jpti.785

Abstract

Jamur memiliki peran penting dalam keanekaragaman hayati, namun beberapa spesies seperti Agaricus xanthodermus dan Amanita muscaria bersifat beracun dan dapat menyebabkan risiko kesehatan serius jika dikonsumsi. Identifikasi jamur beracun secara akurat menjadi tantangan karena kemiripan morfologinya dengan spesies non-beracun. Penelitian ini mengusulkan model klasifikasi jamur beracun menggunakan metode transfer learning dengan arsitektur MobileNetV2, yang dikenal efisien dalam memproses data visual. Dataset terdiri dari 632 gambar, masing-masing 304 gambar untuk Agaricus xanthodermus dan 328 gambar untuk Amanita muscaria, yang diperoleh dari platform Kaggle dan dibagi menjadi 80% data latih dan 20% data validasi. Augmentasi data seperti rotation, shift, flipping, dan rescaling diterapkan untuk meningkatkan generalisasi model. Eksperimen dilakukan dengan menguji pengaruh jumlah epoch terhadap performa model, menggunakan rentang 10 hingga 100 epoch dengan interval 10. Hasil menunjukkan bahwa akurasi model meningkat seiring bertambahnya jumlah epoch, dengan performa optimal pada epoch ke-60. Pada epoch ini, akurasi validasi mencapai 99.21% dengan nilai loss validasi terendah sebesar 0,0447, menunjukkan bahwa model mampu mengklasifikasikan kedua spesies jamur secara akurat dan efisien. Selain itu, tren akurasi dan loss pada data pelatihan menunjukkan bahwa model mampu belajar secara stabil dan tidak mengalami overfitting, bahkan ketika menggunakan dataset yang relatif kecil. Penelitian ini berkontribusi pada pengembangan metode klasifikasi jamur beracun yang lebih akurat dan efisien, yang memiliki implikasi penting dalam kesehatan masyarakat dan konservasi keanekaragaman hayati.
Co-Authors ACHMAD JAELANI RUSDI Ahmad Afif Supianto Ahsanun Naseh Khudori Ali Hasan, Hildan Adisqi Anis Ansyori Ardimansyah Ardimansyah Arif Wahyudi Baskoro, Danang Bagus Choirunnisa, Rosyida Dio Wahyu Saputra Dita Kurnia Rachmasari Dyah Utami, Kartika 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 Mufid Musthofa Muhammad Hasbi Musthofa, Mufid Naufal Alif Vivaldi Nindynar Rikatsih Nindynar Rikatsih Ningrum, Afifah Vera Ferencia Fitria Novelia Mega Puspita Nugroho Teguh Yuono Pradini, Risqy Siwi Prasetiyo, Bagus Dadang Putri, Irnanda Septian Ika Putrinugroho, Ivana Aikozora R. A. Rengganis Ularan Risqy Siwi Pradini Saletia, Maria Claudia Samuel PD Anantadjaya Saputra, Dio Wahyu Siwi Pradini, Risqy Susi Indriyani Wahyu Tejakusuma, Wahyu Wayan Firdaus Mahmudy Wibowo, Heri Wahyu Yahya, Furqon Nurbaril Yuono, Nugroho Teguh