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All Journal Jurnal Penelitian Kesehatan Suara Forikes MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Manajemen Informasi Kesehatan Indonesia (JMIKI) Jurnal Manajemen dan Administrasi Rumah Sakit Indonesia (MARSI) Jurnal Teknologi Informasi dan Multimedia Jurnal Pengabdian Masyarakat IPTEKS Jurnal Aisyah : Jurnal Ilmu Kesehatan Infokes : Jurnal Ilmiah Rekam Medis dan Informasi Kesehatan Jurnal Kesehatan Tambusai J-REMI : Jurnal Rekam Medik dan Informasi Kesehatan Journal of Community Development BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Jurnal Rekam Medik & Manajemen Informasi Kesehatan (RAMMIK) Jurnal Pengabdian Literasi Digital Indonesia International Journal of Health and Information System (IJHIS) Health & Medical Sciences International Journal of Healthcare and Information Technology Journal of Digital Literacy and Volunteering Manajemen Pelayanan Kesehatan Journal of Public Health and Community Systems
Muhammad Yunus
Politeknik Negeri Jember
Religion Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Health Professions Library & Information Science Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary

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Pemetaan Literatur Sistem Digital Pemantauan Anak: Menyusun Kerangka Kebutuhan untuk Platform E-Kesehatan Terintegrasi di Indonesia Erna Selviyanti; Gandu Eko Julianto Suyoso; Mudafiq Riyan Pratama; Muhammad Yunus
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 1 (2026): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i1.197

Abstract

Background: Child growth and development monitoring is a fundamental intervention in preventing stunting and developmental delays. Although various digital systems have been developed, fragmentation between physical growth and child development monitoring remains a major challenge. Objective: This study aims to map the research landscape of digital systems for child growth and development monitoring and identify gaps and the most frequently appearing system requirements in the literature. Method: A systematic mapping study was conducted on six data sources consisting of direct access and Publish or Perish searches, including Scopus, Scopus via PoP, PubMed, PubMed via PoP, IEEE Xplore, and Web of Science via PoP for the 2020-2025 period. A total of 16 research articles were analyzed using VOSviewer for bibliometric mapping and thematic analysis for system requirements identification. Results: The mapping revealed six research clusters with digital health positioned peripherally. Temporal analysis showed a shifting focus toward mental health integration, stunting prevention, and digital tool utilization. Three main gaps were identified: the peripheral position of digital health, separation between growth and development domains, and dominance of observational studies. The most dominant functional requirements were growth monitoring (12 articles) and developmental screening (11 articles), while usability (9 articles) was the main non-functional priority. Conclusion: Integration of growth and development monitoring in a single platform remains understudied. The resulting requirements list can serve as an initial reference for future integrated system development.
Sistem Deteksi Dini Diabetes Melitus Dengan Teknik Klasifikasi Algoritma C4.5 Berdasarkan Rekam Medis di RS Tk. III Baladhika Husada Jember Alviani Rodyatul Agustina; Mudafiq Riyan Pratama; Muhammad Yunus; Ervina Rachmawati
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 1 (2026): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i1.203

Abstract

Diabetes Mellitus (DM) is a chronic disease condition that occurs when the pancreas cannot produce insulin or when the body cannot use insulin effectively. At Baladhika Husada Jember Hospital, DM ranks among the top 10 diseases with the highest mortality rate of 6.99% in 2024. In efforts to prevent and control DM, a website-based early detection system was developed using the C4.5 algorithm classification technique with the Waterfall method. The research stages included creating C4.5 algorithm classification rules using RapidMiner tools, followed by development using the Waterfall method, which consists of the communication, planning, modeling, construction, and deployment stages. The classification rules were developed using preprocessed data from a total of 240 datasets, resulting in 172 clean datasets obtained from medical records at Baladhika Husada Jember Hospital. The training and testing data ratio was 50:50 using stratified sampling. Performance testing using the Confusion Matrix method yielded accuracy, precision, and recall values of 100% each, along with 8 classification rules that were subsequently implemented in the system. Based on the research results, random blood sugar is the most influential risk factor for DM, as it achieved the highest gain ratio. Recommendations for future researchers include increasing the amount of data and expanding the variety of data to help the system learn more complex patterns.
Sistem Deteksi Dini Diabetes Mellitus Berdasarkan Rekam Medis Menggunakan Algoritma K-Nearest Neighbor Arleni Aulia Yunitasari; Mudafiq Riyan Pratama; Muhammad Yunus; Gandu Eko Julianto Suyoso
BIOS : Jurnal Teknologi Informasi dan Rekayasa Komputer Vol 7 No 1 (2026): March
Publisher : Puslitbang Sinergis Asa Professional

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37148/bios.v7i1.204

Abstract

At RSD dr. Soebandi Jember, 51% of Diabetes Mellitus (DM) patients are diagnosed after complications occur, and DM is the third leading cause of death among non-communicable diseases, accounting for 13.6%. This situation indicates a high rate of delayed case identification. Delayed diagnosis significantly increases patient mortality and morbidity rates, emphasizing the urgent need for an effective, integrated early, and detection system. This study developed a web-based early detection system for DM using the K-Nearest Neighbor (K-NN) algorithm with the Waterfall development method, consisting of the stages of communication, planning, modeling, construction, and deployment. The data comprised from 342 inpatient medical records, and after preprocessing, 164 clean data were obtained with variables including age, gender, family history, blood pressure, random blood sugar, and body mass index. The data were split using stratified sampling (50:50), with K=5 value selected based on the best performance. Blackbox testing was conducted to ensure the system’s functionality, while performance testing compared the system’s classification results with the test data. The performance of the K-NN algorithm for DM detection was evaluated using a Confusion Matrix, resulting in an accuracy of 97.56%, precision of 100%, and recall of 95.83%, which were consistent with the results from the WEKA tool. This system is expected to serve as an early screening tool and support DM prevention efforts.
Sleep Disorder Classification Using the Naïve Bayes Algorithm Olivia Natania Anjani; Khusnul Fatimah Azzahra; Anisa Fitria Rahma; Aida Rahma Saqina; Muhammad Yunus
Journal of Public Health and Community Systems Vol. 1 No. 1 (2026): June
Publisher : PT Litera Integra Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67580/nexura.v1i1.8

Abstract

Sleep disorders are a medical condition with a continuously increasing prevalence globally, including in Indonesia, and have a serious impact on an individual’s quality of life, cognitive function, and cardiovascular health. This study aims to classify types of sleep disorders using the Naïve Bayes algorithm as a probabilistic approach in health data mining. The dataset used is the Sleep Health and Lifestyle Dataset, consisting of 400 data records with 13 attributes, covering demographic factors, lifestyle, and physiological parameters. The research stages included data preprocessing, blood pressure feature engineering, and splitting the data into training and test sets with an 80:20 ratio. Model evaluation was performed using a confusion matrix and accuracy, precision, recall, and F1-score metrics. The results showed that the Naïve Bayes model achieved an accuracy of 71.60% on the 81-sample test set. The best performance was observed in the "None" class with an F1-score of 0.83, while the "Insomnia" and "Sleep Apnea" classes could not be successfully identified by the model due to the dominance of the majority class (class imbalance). An example prediction for a 47-year-old female subject with a sleep quality of 7/10, a stress level of 4/10, and a Normal BMI resulted in a classification of "No Sleep Disorder" with a probability of 72.08%. These findings indicate that addressing class imbalance is necessary to improve the classification performance of the Naïve Bayes model on the sleep disorder dataset.
Breast Cancer Classification Using the C4.5 Method with the Breast Cancer Wisconsin Dataset Najwa Meilani; Intan Novitasari; Aprilia Wulandari; Naurah Ananda; Muhammad Yunus
Journal of Public Health and Community Systems Vol. 1 No. 1 (2026): June
Publisher : PT Litera Integra Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.67580/nexura.v1i1.14

Abstract

Breast cancer is one of the most life-threatening diseases in the world, particularly among women. This study applies the C4.5 algorithm to classify breast cancer using the Breast Cancer Wisconsin (Diagnostic) Dataset from the UCI Machine Learning Repository, consisting of 569 samples with 30 numerical features. The methods employed include data preprocessing (removal of the ID column), application of the Decision Tree algorithm with entropy criterion representing the Information Gain Ratio in a Python- and Streamlit-based implementation, and model evaluation using an 80:20 data split. Experimental results show that the C4.5 model achieves an accuracy of 95.6%, with an average Precision of 95.7%, average Recall of 95.6%, and average F1-Score of 95.6%. The perimeter_worst attribute was identified as the root node of the decision tree with a threshold value of 114.45, confirming the dominant role of tumor cell geometry size as a predictor of malignancy. This study concludes that the C4.5 algorithm is an effective and interpretable approach for breast cancer classification and has the potential to serve as the basis for a clinical decision support system in oncology.
Co-Authors Adinda Zulaikha Aghasi Hana Faradila Aida Rahma Saqina Ain Azzam Izulhaq Akbar Juliansyah Alfiansyah, Gamasiano Ali Chamid Alviani Rodyatul Agustina Andi Miftahul Jannah Ramlan Andri Permana Wicaksono Angga Rahagiyanto Anisa Fitria Rahma Aprilia Wulandari Arleni Aulia Yunitasari Arniati Ramadhani Atma Deharha Atma Deharja Atma Deharja Atma Deharja Avivah Nur Aini Awiera Shalshabila Bakhtiyar Hadi Prakoso Deharja, Atma Demiawan Rachmatta Putro Mudiono Detty Artin Meirina Devi Kalita Verdilasari Dinda Meidy Herdayanti dony setiawan hendyca putra Dyah Susilowati Erna Selviyanti Febriyani, Kharishma Dyah Feby Erawantini Feby Erawantini Firdha Trisna Andriani Gamasiano Alfiansyah Gamasiano Alfiansyah Gandu Eko Julianto Suyoso Ihab Majdi Inas Fadhilah 'Allam Zari Intan Novitasari Khusnul Fatimah Azzahra Lalu Hadi Ichlasul Amal Lani Lani Annisa Majiida Liana Ulfa M. Choirur Roziqin M. Rodi Taufik Akbar Maya Weka Santi Maya Weka Santi Meiranda Normarisa Azis Mochammad Choirur Roziqin Mohammad Maulana Rifki Fadilah Mudafiq Riyan Pratama Muhammad Misbahul Muttaqiin Muhammad Naufal Mustafa Mulianingsih Samsir Najwa Meilani Naurah Ananda Ni Kadek Ari Pratiwi Niyalatul Muna Niyalatul Muna Novita Nuraini Novita Shanty Wulandari Nur Faiza Hardiyanti Nur' Aini nurmawati, ida Olivia Natania Anjani Pahrul Irfan Prakoso, Bakhtiyar Hadi Qotrul Imdadi Syawqoni Rachmawati, Ervina Rahagiyanto, Angga Rony Arzian Rossalina Adi Wijayanti S Deddy Setiadi Sabran Sabran Sonia Putri Haris Fadilah Suci Apriani Ayudi Suhartanty Afifah Rahmatillah Sustin Farlinda Sutrisno Sutrisno Suyoso, Gandu Eko Julianto Veronika Vestine Vestine, Veronika Zaenal Abidin Zahwa Laila Primadesi Zainul Hasan