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All Journal International Journal of Electrical and Computer Engineering International Journal of Advances in Applied Sciences Jurnal Kesehatan Hesti Wira Sakti Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Journal of Information Technology and Computer Science Jurnal Penelitian Kesehatan Suara Forikes InComTech: Jurnal Telekomunikasi dan Komputer Jurnal Keperawatan Muhammadiyah Jurnal Ilmiah Keperawatan (Scientific Journal of Nursing) JOISIE (Journal Of Information Systems And Informatics Engineering) Jurnal Ilmu Komputer dan Desain Komunikasi Visual Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) CICES (Cyberpreneurship Innovative and Creative Exact and Social Science) Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Engineering, Mathematics and Computer Science Journal (EMACS) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Jurnal Teknologi Kesehatan International Journal Software Engineering and Computer Science (IJSECS) Jurnal Pengabdian Masyarakat Bhinneka Malcom: Indonesian Journal of Machine Learning and Computer Science Prosiding Seminar Nasional Pengabdian Kepada Masyarakat Journal of Artificial Intelligence and Digital Business Jikom: Jurnal Informatika dan Komputer Jurnal Indonesia : Manajemen Informatika dan Komunikasi The Journal of Enhanced Studies in Informatics and Computer Applications Jurnal Pengabdian Masyarakat Teknologi Informasi dan Informatika
Wahyu Tejakusuma, Wahyu
Institut Teknologi, Sains, Dan Kesehatan RS.DR. Soepraoen Kesdam V/BRW
Agriculture, Biological Sciences & Forestry Arts Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences 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 Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Physics Public Health

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MOBILE APPLICATION DEVELOPMENT FOR CHRONIC DISEASES RECORDING OF ARMY MEMBERS Haris, M Syauqi; Bagus Prasetyo Abdi , Benben; Teja Kusuma, Wahyu
Jurnal Mnemonic Vol 8 No 1 (2025): Mnemonic Vol. 8 No. 1
Publisher : Teknik Informatika, Institut Teknologi Nasional malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/mnemonic.v8i1.13693

Abstract

Health services for army personnel play a crucial role in supporting military readiness and national defense. However, the current chronic disease data collection for army members is still conducted manually, causing inefficiencies in updating health information and hindering prompt promotive and preventive actions. This situation highlights the urgency for an integrated and accessible information system that allows health officers to monitor chronic illness cases more efficiently, especially in supporting early interventions and reducing curative and rehabilitative workloads. This research presents the development of a Mobile Device Application designed specifically for chronic disease data recording among army members, implemented in the Brawijaya Military Regional Health Unit (Kesdam V Brawijaya). The application, which is an expansion of the previously developed RAPI (Realtime Accountable Professional Integrative) system, utilizes Progressive Web App (PWA) technology to ensure accessibility and flexibility for users. The development followed the System Development Life Cycle (SDLC) methodology, with features tailored to stakeholder requirements, such as data grouping, diagnosis tracking, check-up history, and real-time reporting. The testing phase showed valid results for all system functionalities and confirmed compatibility across various smartphone types. The application successfully met all functional specifications and user needs, enabling faster access to health data, supporting preventive action planning, and reducing delays in chronic disease treatment monitoring. In conclusion, this application offers an effective, user-friendly, and scalable solution to improve health service delivery and chronic disease management for military personnel
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.
Analisis Usability Website Berbinar Insightful Indonesia Menggunakan USE Questionnaire dan Performance Test Shaktyanti, Frenchyani Anggi; Pradini, Risqy Siwi; Kusuma, Wahyu Teja
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 2 (2025): Mei
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i2.1387

Abstract

This research analyzes the usability of the Berbinar Insightful Indonesia website using a use questionnaire and performance test to test the suitability of this website. Testing was carried out by giving 5 task scenarios and 30 questionnaires to 20 participants. Testing using task scenarios is very important for measuring the performance of a website through user experience. The research instrument used is a use questionnaire used to calculate the usability value of a website. Based on the results of the use questionnaire, the usability value was found to be 79% in the good category, apart from that, the value of the 4 aspects of the use questionnaire, namely, usefulness was equal to, ease-of-use was equal to, ease-of-learning was equal to and satisfaction was equal to. Furthermore, the results of the performance test show the participant's time to complete the task, the number of participant errors and mistakes, the participant's success rate as well as a time efficiency value of 79% and an overall relative efficiency of 98%. These findings contribute to usability testing on the Berbinar Insightful Indonesia website based on user experience.
IMPLEMENTASI MACHINE LEARNING DALAM DETEKSI BUG OTOMATIS PADA KODE SUMBER OPEN SOURCE Pitunas, Hery; Teja Kusuma, Wahyu; Naseh Khudori, Ahsanun
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.13965

Abstract

Dalam pengembangan software, menghilangkan bug penting untuk menjaga kualitas aplikasi. Developer sering menghabiskan banyak waktu untuk mengatasi dan menemukan solusi bug yang dialami. Beragamnya jenis bug membuat proses ini menjadi semakin kompleks dan memakan waktu. Berdasarkan hal tersebut, klasifikasi bug menjadi solusi penting. Data penyelesaian bug yang tersedia secara public dapat dimanfaatkan untuk klasifikasi dan prediksi otomatis menggunakan machine learning. Penelitian ini menerapkan KNN untuk mendeteksi bug otomatis pada kode sumber open source. Hasilnya menunjukkan bahwa parameter k dalam KNN dan k-fold pada cross-validation berpengaruh signifikan terhadap performa model. Nilai k yang kecil membuat model lebih sensitive terhadap noise dan rentan overfitting, sedangkan nilai k ynag besar meningkatkan stabilitas dan generalisasi meskipun akurasi menurun. K-fold yang lebih besar menghasilkan model yang lebih stabil dengan akurasi yang lebih tinggi. Akurasi terbaik yang diperoleh adalah 0,9025 dengan presisi, recall, dan f-measure yang perlu dioptimalkan.
IMPLEMENTASI DECISION TREE UNTUK KLASIFIKASI OBAT PASIEN DIABETES DAN HIPERTENSI Rikatsih, Nindynar; Dadang Prasetiyo, Bagus; Teja Kusuma, Wahyu
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 9 No. 4 (2025): JATI Vol. 9 No. 4
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v9i4.14038

Abstract

Diabetes dan hipertensi merupakan dua penyakit kronis yang sering terjadi bersamaan dan dapat memicu komplikasi serius seperti penyakit kardiovaskular, stroke, dan gangguan ginjal. Penanganan kedua penyakit ini seringkali bersifat subjektif, tergantung pada pengalaman tenaga medis, padahal kondisi fisiologis setiap pasien berbeda. Seiring perkembangan teknologi, data mining dapat dimanfaatkan untuk membantu pengambilan keputusan medis berbasis data. Penelitian ini bertujuan untuk mengklasifikasikan jenis obat yang diberikan kepada pasien diabetes dan hipertensi menggunakan algoritma C4.5. Hasil klasifikasi disusun dalam bentuk aturan “if-then” dan dievaluasi menggunakan teknik cross-validation untuk menghindari overfitting. Model yang dihasilkan menunjukkan akurasi tinggi sebesar 94,5% dengan presisi 94,6%, recall 94,5%, dan f-score 94,5%. Temuan ini menunjukkan bahwa pendekatan data mining berpotensi mendukung pengambilan keputusan yang lebih tepat dalam pelayanan medis terhadap pasien diabetes dan hipertensi.
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.
Perancangan Aplikasi Terapi Musik untuk Penderita Anxiety Menggunakan Pendekatan Human Centered Design (HCD), Persona, dan Minimum Viable Product (MVP) Dyah Utami, Kartika; Kusuma, Wahyu Teja; Anshori, Mochammad
Jurnal Indonesia : Manajemen Informatika dan Komunikasi Vol. 6 No. 3 (2025): September
Publisher : Lembaga Penelitian dan Pengabdian Kepada Masyarakat (LPPM) STMIK Indonesia Banda Aceh

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63447/jimik.v6i3.1453

Abstract

Anxiety represents a prevalent mental disorder among individuals in their productive years, significantly affecting quality of life and psychological well-being. The present study aimed to design a music therapy application as a non-pharmacological intervention for alleviating anxiety symptoms. Nineteen respondents aged 20–24 years with diagnosed anxiety disorders in the Malang region participated in the study. The application was developed using Human-Centered Design (HCD) approaches, persona methodology, and Minimum Viable Product (MVP) principles. Therapeutic music categories included guided meditation tracks, nature sounds, and 432Hz frequency compositions, all validated by a hypnotherapy specialist. Prototype evaluation was conducted through user needs mapping (persona goals) and expert validation procedures. Results demonstrate that the prototype successfully addressed three core indicators: PG.1 (access to therapeutic music catalog), PG.2 (music search functionality), and PG.3 (playback interface design). These features were engineered to deliver accessible relaxation experiences for independent use. The research establishes that integrating HCD, persona, and MVP methodologies can generate solutions grounded in authentic user requirements. Study limitations include the restricted sample size and absence of long-term effectiveness assessment. Future research should investigate emotion-based music personalization features to enhance therapeutic outcomes.
Perancangan Aplikasi Resep Makanan Pencegah Stunting Menggunakan Metode Design Sprint francisca, khollydayyana mitra; Kusuma, Wahyu Teja; Khudori, Ahsanun Naseh
Jurnal Informatika dan Komputer Vol 15 No 2 (2025): Oktober
Publisher : Sekolah Tinggi Ilmu Komputer PGRI Banyuwangi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55794/jikom.v15i2.290

Abstract

Stunting is a health problem that is still a serious concern in Indonesia because of its impact on child growth. One of the main factors causing it is the lack of nutritional education for mothers and parents. To help overcome this, this study designed a food recipe application to prevent stunting using the design sprint method combined with persona, crazy 8's, and MVP (Minimum Viable Product). This combination of methods was chosen because it allows the application design process to be carried out quickly, and based on user needs. The design of this application aims to provide information about healthy food menus that are in accordance with children's nutritional needs, especially in preventing stunting. The design of the application user interface (prototype) was developed based on mobile using figma as a design tool. The application design was evaluated using the SUS method with a result of 77 which means that this application has a good level of usability with the Adjective Rating "GOOD" included in the grade C category. The results of this study indicate that this application has the potential to help parents, pregnant women in choosing and serving healthier and more nutritious food for children. With this application, it is hoped that the stunting rate in Indonesia can be reduced through easier and more practical access to nutritional information.
Co-Authors Achmad Fariz Agung Hadi Endaryanto Agung Setia Budi, Agung Setia Agus Purnomo Rijal Kurniawan Ahmad Afif Supianto Ahmad Afif Supianto Ahsanun Naseh Khudori Ahsanun Naseh Khudori Ainun, Nurul Andy Setyawan Anwar, David Saiful Bagus Prasetyo Abdi , Benben Binarti Widyanti Binti Karimatun Nikmah Carrina A. Tesa Choirunnisa, Rosyida Danang Bagus Baskoro Devi Ayu Trisnaningrum Dio Putra Mahardhika Djajalaksana, Susanthy Dyah Utami, Kartika Eko Sakti Pramukantoro, Eko Sakti Emi Aprilyanti Farah Aulia Nisa Faurika Febriantama, Mohammad Dhany Febry Andrias francisca, khollydayyana mitra Hargiani, Fransisca Xaveria Henokh Kristiawan Heri Wahyu Wibowo Herman Tolle Javier Fajri Zachary Leny Candra Kurniawan Lili Octaviani Lola Novira Risdiyanti M. Syauqi Haris Magdalena Amelia Rizky Mochammad Anshori Nindynar Rikatsih Novianto Agus Prabowo Nugroho Teguh Yuono Nurul Halimah Pitunas, Hery Pradini, Risqy Siwi Pradita, Angria Prasetiyo, Bagus Dadang Provisa Redy Fernando Putrinugroho, Ivana Aikozora Retno Widaningrum Rikatsih, Nindynar Risa Puspita Sari Risqy Siwi Pradini Sartoyo Savril Daffa Geraldo, Jeffana Setiawan Edi Kriswanto Shaktyanti, Frenchyani Anggi Silvia Intan Wardani Sri Lestari Wati Supatmi, Eny Usfania Fernanti Vincentia Kristanti Wardoyo, Puspo Wibowo, Heri Wahyu Wirpan Atmaja Putra Yuono, Nugroho Teguh