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All Journal JURNAL MAHAJANA INFORMASI Jurnal Teknik Informatika C.I.T. Medicom JUKI : Jurnal Komputer dan Informatika Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Informatics, Economics, Management and Science Jurnal Pengabdian Pada Masyarakat METHABDI Indonesian Journal of Science, Technology, and Humanities Jurnal Penelitian Teknologi Informasi dan Sains Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Repeater: Publikasi Teknik Informatika dan Jaringan Merkurius: Jurnal Riset Sistem Informasi dan Teknik Informatika Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi Pascal: Journal of Computer Science and Informatics
Melda Pita Uli Sitompul
STMIK Kaputama
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Diagnosa Penyakit Hisprung pada Bayi menggunakan Metode Dempster Shafer Nadia Nurhafiza; Rusmin Saragih; Melda Pita Uli Sitompul
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 6 (2025): November: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i6.1140

Abstract

Hirschsprung’s disease is a congenital disorder caused by abnormal nerve cell development in the large intestine, leading to chronic intestinal obstruction in infants. This condition often manifests through symptoms such as constipation, abdominal distension, vomiting, and failure to thrive. The weak immune system of infants makes them highly susceptible to bacterial infections and further complications. At Bidadari General Hospital, there were 110 patients suspected of having Hirschsprung’s disease. One of the major challenges in managing these cases is the limited number of medical specialists, particularly pediatricians and pediatric surgeons, resulting in long waiting times for accurate diagnosis, especially during peak service hours. To address this issue, this study applies the Dempster-Shafer method in an expert system to assist in diagnosing Hirschsprung’s disease based on clinical symptoms. The method effectively handles uncertainty and combines multiple pieces of medical evidence to produce more accurate diagnostic probabilities. The analysis results show that from the selected symptoms, the highest diagnosis probability corresponds to short-segment Hirschsprung’s disease with a confidence level of 71.54%. These findings suggest that the Dempster-Shafer method can serve as an effective alternative tool to support early and accurate diagnosis of Hirschsprung’s disease in infants.
Pengelompokan Data Siswa SMP dalam Mendeteksi Kesehatan Remaja Menggunakan Algoritma K-Means Delvi Kibina Br Sembiring; Khairul Khairul; Melda Pita Uli Sitompul
Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika Vol. 3 No. 6 (2025): November: Merkurius : Jurnal Riset Sistem Informasi dan Teknik Informatika
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/merkurius.v3i6.1142

Abstract

Technological advancements in education have led to major transformations, particularly with the implementation of the Merdeka Curriculum, which emphasizes learning flexibility, student-centered approaches, and educator autonomy in developing innovative teaching methods. One of its essential aspects is the integration of technology for managing educational data, including student health records. At SMP IT Mutia Rahma, biannual student health monitoring has generated a growing volume of data, making it difficult to identify students experiencing psychological challenges. Adolescent mental health problems—such as learning stress, anxiety, and social pressure—can negatively affect academic performance if left unaddressed. This study aims to group students based on their mental health conditions to support more effective intervention strategies. The K-Means Algorithm, a data mining technique for clustering data by similarity, was employed to analyze student health data. The results show that in a three-cluster model, Cluster 2 represents students in a stable condition characterized by high resilience and low counseling needs, indicating good mental health and academic engagement. Meanwhile, Clusters 1 and 3 include students requiring further attention and support. This research demonstrates that the K-Means Algorithm can serve as an effective tool in identifying and categorizing student mental health conditions to improve school-based health management and early intervention programs.
Decision Support System for Determining Effective Learning Strategies for Students Using the SMART Method Athaya, Fara; Simanjuntak, Magdalena; Sitompul, Melda Pita Uli
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 02 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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

Abstract

Effective learning strategies are essential factors in improving students’ academic achievement. However, at SMP Negeri 2 Binjai, several challenges remain, including the low effectiveness of applied learning methods, the lack of adaptation to individual learning styles, and the limited use of academic data in supporting learning decisions. These issues were further exacerbated by the post-pandemic shift toward hybrid learning models, which has not been fully optimized. To address this problem, this study designed a Decision Support System (DSS) using the SMART (Simple Multi-Attribute Rating Technique) method to recommend suitable learning strategies for students. The system was developed through stages of requirement analysis, logical design of the SMART calculation, and the implementation of integrated multi-criteria processing. The results show that the system can provide objective and accurate learning strategy recommendations. From 32 students analyzed, 11 students (34.37%) were recommended to adopt E-learning, 7 students (21.87%) to use Blended Learning, and 14 students (43.75%) to apply Traditional Learning. The highest score of 1.00 was achieved by two students in the E-learning category, while the lowest score of 0.125 was recorded in the Traditional category. These findings confirm that the application of the SMART method in DSS is effective in helping teachers and students determine more adaptive and personalized learning strategies, thereby supporting the improvement of learning quality in schools.
Diagnosa Penyakit Kelamin (Vulvodynia) pada Wanita Menggunakan Metode Certainty Factor Zian Sari; Marto Sihombing; Melda Pita Uli Sitompul
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 4 (2024): November: Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i4.215

Abstract

Vulvodynia is a chronic pain condition affecting the vulva that significantly impacts women’s quality of life. Accurate and early diagnosis poses a challenge due to the often-overlapping symptoms with other conditions and the lack of definitive diagnostic tests. This paper proposes the use of expert system methods as a diagnostic tool for vulvodynia in women. The expert system, integrating medical knowledge with inference algorithms, is designed to analyze symptoms, medical history, and test results to provide accurate diagnoses and treatment recommendations. The study involves the development and evaluation of a computer-based expert system prototype that uses clinical data and medical decision-making to enhance the accuracy of vulvodynia diagnosis. Preliminary results indicate that the expert system can improve diagnostic rates and reduce the time required for identifying this condition, offering a potentially valuable tool for medical professionals in clinical practice.
Sistem Pakar Diagnosa Penyakit Autoimun Menggunakan Metode Dempster Shafer M. Rizki Auliansyah Ginting; Akim M.H. Pardede; Melda Pita Uli Sitompul
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 4 (2024): November: Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Profesi Telekomunikasi Dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i4.222

Abstract

Autoimmune diseases, which are disorders where the immune system attacks the body’s own tissues, can affect anyone, including children and adults. These diseases often lead to serious tissue damage and physiological disturbances. Al Fuadi Binjai General Hospital, the primary healthcare facility in Binjai City, faces challenges in diagnosing autoimmune diseases in a timely manner due to limitations in time, cost, and distance. Delays in treatment can exacerbate patient conditions and slow recovery processes. The objective of this study is to develop a system that processes symptom and autoimmune disease data using the Dempster-Shafer method, which allows for uncertainty assessment in decision-making. Patient symptom data collected and analyzed using this method aims to determine the likelihood of autoimmune diseases. The developed system demonstrated high diagnostic accuracy, with the most accurate results for lupus with a confidence level of 94.40%. This result indicates that the Dempster-Shafer method can be an effective tool in accelerating the diagnostic process and improving the accuracy of autoimmune disease management at Al Fuadi Binjai General Hospital
Application of Apriori Algorithm in Determining Behavioral Patterns and Lifestyle of GERD Patients pramudhita, chika; Buaton, Relita; Sitompul, Melda Pita Uli
International Journal of Informatics, Economics, Management and Science Vol 3 No 2 (2024): IJIEMS (August 2024)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/ijiems.v3i2.1593

Abstract

Gastroesophageal Reflux Disease (GERD) is a condition of reflux of stomach contents into the esophagus which can cause typical symptoms such as heartburn (burning in the epigastric region), acid regurgitation (bitter taste in the mouth), nausea, and dysphagia which can result in damage to the esophageal mucosa and in the long term can cause complications such as Barrett's esophagus. Based on data on patients with GERD disease in 2020-2023, there were 521 patients obtained at Bidadari Binjai General Hospital, with differences in prevalence caused by socioeconomic and lifestyle changes that can increase the incidence of GERD. The purpose of this study is to obtain the results of the combination between item-sets on the behavior patterns and lifestyles of GERD sufferers which are expected to help the agency in processing data on GERD sufferers into more effective information to determine the behavior patterns and lifestyles of GERD sufferers with the Apriori Algorithm method. From the research conducted, the results obtained rules that meet the value of 30% support and 100% confidence, if unhealthy eating patterns are often late eating, then unhealthy lifestyles are often done sleeping after eating and smoking.
Pelatihan Pembuatan Eco Enzym bagi Mahasiswa Baru STMIK Kaputama TA. 2024-2025 Sebagai Bentuk Kepedulian Kampus pada Pelestarian Lingkungan Arliana, Lina; Maulita, Yani; Fauzi, Ahmad; Novriyenni, Novriyenni; Sihombing, Anton; Ambarita, Indah; Simanjuntak, Magdalena; Syahputra, Siswan; Khair, Husnul; Pramana, I Gusti; Annatasia, Kristina; Puspadini, Ratih; Selfira, Selfira; Sitompul, Melda Pita Uli; Khadafi, Muammar
Jurnal Pengabdian Pada Masyarakat METHABDI Vol 5 No 1 (2025): Jurnal Pengabdian Pada Masyarakat METHABDI
Publisher : Universitas Methodist Indonesia

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

Abstract

The growing volume of household organic waste poses a serious challenge to environmental sustainability. Through this community service program, STMIK Kaputama offers an educational and practical solution by involving first-year students in Eco Enzyme production training. Eco Enzyme, a fermented product derived from organic kitchen waste, has been proven to provide various benefits such as an eco-friendly cleaner, liquid fertilizer, and natural deodorizer, serving as an effective alternative to harmful chemical-based products. During the student orientation program (PKKMB), 300 students participated in a series of activities, including environmental education, fermentation practice, and harvesting of Eco Enzyme. The results demonstrated improvements in students’ knowledge and skills, with the expectation of long-term behavioral changes in managing waste at its source. The program successfully produced more than 300 liters of Eco Enzyme and significantly reduced organic waste disposal to landfills. More importantly, it cultivated environmentally conscious student leaders. Engaging first-year students in this real-world initiative serves as a strategic effort to instill sustainability values and foster a culture of environmental responsibility both within the campus and the broader community.
Co-Authors ACHMAD FAUZI Ambarita, Indah Anisa Putri Pratiwi Annatasia, Kristina Arliana, Lina Athaya, Fara Br Sitepu, Kristina Buaton, Relita Delvi Kibina Br Sembiring Diana Lestari Simamora Fauzi Ahmad Muda Fauzi, Achmad Kahfi Lanang Khair, Husnul Khairul Khairul M. Naufal Syahputra M. Rizki Auliansyah Ginting Marto Sihombing MHD Micho Januar Prananta Muammar Khadafi Nadia Nurhafiza Novriyenni, Novriyenni Nur Fariza Khairani Opim Salim Sitompul Pardede, Akim Manaor Hara Pramana, I Gusti Pramudhita, Chika Rahimah Faizah Ranti, Dwi Ratih Puspadini Rizki Tri Ipanda Rusmin Saragih, Rusmin Selfira, Selfira Sihombing, Anton Simanjuntak, Magdalena Syahputra, Siswan Yani Maulita Zakarias Situmorang Zian Sari