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All Journal JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Informatika Kaputama (JIK) Jurnal Sistem Informasi Kaputama (JSIK) JURNAL MAHAJANA INFORMASI JTIK (Jurnal Teknik Informatika Kaputama) MEANS (Media Informasi Analisa dan Sistem) Journal of Artificial Intelligence and Engineering Applications (JAIEA) International Journal of Informatics, Economics, Management and Science Journal of Engineering, Technology and Computing (JETCom) Journal of Mathematics and Technology (MATECH) Jurnal Pengabdian Pada Masyarakat METHABDI Jurnal ABDIMAS MUTIARA Pascal: Journal of Computer Science and Informatics JURNAL TEKNOLOGI, KESEHATAN DAN ILMU SOSIAL
Simanjuntak, Magdalena
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APPLICATION OF NAIVE BAYES METHOD TO DIAGNOSE FMD DISEASE IN GOATS Sawitri, Sawitri; Simanjuntak, Magdalena; Pardede, Akim Manaor Hara
Journal of Mathematics and Technology (MATECH) Vol. 3 No. 2 (2024): Journal MATECH (November 2024)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v3i2.171

Abstract

Hoof and mouth disease (FMD) is an infectious disease that affects cloven-hoofed farm animals such as cows, buffaloes, goats, sheep and pigs. The emergence of FMD is caused by a virus called foot and mouth diseases virus (FMVD). The Virus slowly eats away at the hooves and mouths of livestock, making the animals unable to eat and walk. Examination of disease in goats periodically is currently less attention so as to make goats susceptible to disease. This makes it difficult for farmers in the initial handling and do not know what to do in the absence of an expert. The process of disease diagnosis in goats can not be done by just anyone because between the types of diseases with symptoms have uncertainty. Based on these problems, the authors create an expert system that is able to diagnose diseases in goats as is commonly done by an expert using Naive Bayes method that will help livestock groups in diagnosing diseases in goats. The input is in the form of symptoms that occur in the field and the output is the result of diagnosis and treatment advice. From the test results obtained because the conclusion value (P / PMK type Oise (O)) is greater than the value (P|PMK type Asia 1) then the decision is “PMK type Oise (O)” with a value of 0.02.
THE USE OF BAYES METHOD TO DIAGNOSE GESTATIONAL DIABETES IN PREGNANT WOMEN (CASE STUDY: DR. EDWARD JOB,. SP.OG) Maulidina, Nadia; Simanjuntak, Magdalena; Maulita, Yani
Journal of Mathematics and Technology (MATECH) Vol. 3 No. 2 (2024): Journal MATECH (November 2024)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v3i2.175

Abstract

 Gestational Diabetes is the cause of diabetes that occurs during pregnancy, generally, this pregnancy occurs in the second trimester, between Weeks 24 to 28. Gestational Diabetes is one of the causes of death of pregnant women due to lack of information or knowledge about the factors that cause gestational diabetes that occurs in mothers during pregnancy, causing health risks, and affect the decision of time, cost and others in carrying out direct medical consultations. Therefore, it is highly recommended that an expert system for treating gestational diabetes in pregnant women be able to find out early about what is happening and be able to overcome the causes and reduce mortality. With the existence of this system is expected to be an alternative for patients who experience complications of time, cost and others in consulting directly about the treatment of gestational diabetes in pregnant women, before coming directly to meet with a doctor/expert. From the process using the Bayes method above, it is explained that the diagnosis of gestational diabetes in pregnant women is diagnosed with gestational diabetes Diabetes Mellitus (P01) with a percentage of 90.73%.
EXPERT SYSTEM OF PREECLAMPSIA DIAGNOSIS USING CERTAINTY FACTOR METHOD Tarigan, Kiki Dea Ananda; Simanjuntak, Magdalena; Maulita, Yani
Journal of Mathematics and Technology (MATECH) Vol. 3 No. 2 (2024): Journal MATECH (November 2024)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/matech.v3i2.176

Abstract

Preeclampsia is an increase in blood pressure and excess protein in the urine that occurs after more than 20 weeks of pregnancy. If not treated immediately, preeclampsia can cause complications that are dangerous for the mother and fetus. One factor that can increase the risk of preeclampsia is the age of pregnant women who are under 20 years or more than 40 years. This condition needs to be treated immediately to prevent complications or develop into preeclampsia that can threaten the lives of pregnant women and fetuses. The causes of preeclampsia are still not exactly known. However, this condition is thought to occur due to abnormalities in the development and function of the placenta, which is an organ that functions to deliver blood and nutrients to the fetus. The use of internet technology makes it easier for humans to access information without limited space and time and facilitates the design of expert systems to diagnose preeclampsia in mothers, and is expected to reduce or even eliminate existing problems, Therefore, an application is needed that can help to diagnose preeclampsia by using the certainty factor method which is easier and becomes an alternative in providing more knowledge about the results of preeclampsia diagnosis in pregnant women and can provide advice and consultation media about preeclampsia disease in patients and can reduce the cost of consulting an expert. Based on the calculation of CF, the highest value is the type of preeclampsia with a value of 0.9939 or 99.39%. From the results obtained, the system identifies that the patient has a type of preeclampsia.
ANALISIS CLUSTER STUNTING DENGAN METODE K-MEANS DI KOTA BINJAI Buaton, Relita; Maulidya, Adek; Simanjuntak, Magdalena; Sinaga, Ayu Puspita Sari
Journal of Information System, Informatics and Computing Vol 9 No 1 (2025): JISICOM (June 2025)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v9i1.1927

Abstract

Puskesmas berperan penting dalam meningkatkan kesehatan masyarakat. Analisis data kesehatan yang tepat dapat membantu dalam mengidentifikasi kelompok populasi yang membutuhkan perhatian khusus. Sumber daya manusia yang unggul dan berkualitas didasari dengan sumber daya manusia yang sehat dengan indikator tercukupinya asupan gizi sesuai dengan perkembangan usianya. Namun masalah kelaparan dan kekurangan gizi masih dihadapi oleh dunia hingga saat ini. Menurut laporan Unicef, jumlah penduduk yang menderita kekurangan gizi di dunia mencapai 767,9 juta orang pada tahun 2021. Organisasi Kesehatan Dunia (WHO) mengatakan, kekurangan gizi menjadi salah satu ancaman berbahaya bagi kesehatan penduduk dunia. Stunting juga berdampak di Indonesia, prevalensi balita yang mengalami stunting di Indonesia sebanyak 21,6% pada tahun 2022. Penelitian ini bertujuan untuk mengklasifikasikan data kesehatan dari Puskesmas di Binjai menggunakan algoritma K-Means untuk memahami karakteristik setiap kluster.Dilakukan studi lapangan dengan mengolah data hasil penimbangan anak, data diolah dengan menggunakan metode cluster sehingga diperoleh cluster stunting untuk wilayah Kota Binjai yakni kluster 1: mencerminkan kondisi kesehatan yang baik, dengan nilai rata-rata yang rendah pada indikator risiko gizi dan gizi buruk, kluster 2: menunjukkan kondisi yang sangat buruk, dengan nilai yang tinggi pada hampir semua indikator, mencerminkan masalah kesehatan yang serius di populasi tersebut dan kluster 3: menunjukkan kondisi moderat, dengan nilai yang berada di antara kluster 1 dan kluster 2. Hasil analisis menunjukkan bahwa terdapat tiga kluster yang berbeda, masing-masing dengan karakteristik kesehatan yang unik. Pengujian kluster dilakukan dengan menggunakan metode cluster analysis untuk memastikan validitas hasil. Temuan ini diharapkan dapat memberikan rekomendasi bagi pihak dians kesehatan dalam merancang program intervensi kesehatan yang lebih tepat sasaran dengan hasil pengujian Silhouette Score: 0.65, menunjukkan bahwa kluster yang terbentuk cukup baik. Davies-Bouldin Index: 0.3, menunjukkan pemisahan kluster yang baik. Inertia: 1500 menandakan bahwa data terdistribusi dengan baik di sekitar centroid.
OPTIMASI PENJADWALAN MATA KULIAH DENGAN MENGGUNAKAN PSO Simanjuntak, Magdalena; Sitompul, Melda Pita Uli; Juliana Naftali Sitompul; Kahfi Lanang; Rahimah Faizah
Jurnal Mahajana Informasi Vol 10 No 1 (2025): JURNAL MAHAJANA INFORMASI
Publisher : Universitas Sari Mutiara Indonesia Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51544/jurnalmi.v10i1.6062

Abstract

Penjadwalan mata kuliah merupakan masalah kompleks yang sering dihadapi oleh institusi pendidikan tinggi. Ketidakefisienan dalam penjadwalan dapat mengakibatkan konflik jadwal, pemanfaatan ruang yang tidak optimal, serta ketidakpuasan dosen dan mahasiswa. Oleh karena itu, diperlukan metode optimasi yang efektif untuk menghasilkan jadwal yang optimal dan efisien. Penelitian ini bertujuan untuk mengembangkan dan mengimplementasikan algoritma Particle Swarm Optimization (PSO) untuk mengoptimalkan penjadwalan mata kuliah. PSO dipilih karena kemampuannya dalam menangani masalah optimasi dengan ruang solusi yang besar dan kompleks. Algoritma ini diharapkan dapat menghasilkan jadwal yang meminimalkan konflik, mengoptimalkan penggunaan ruang kelas, serta memperhitungkan preferensi dosen dan mahasiswa. Penelitian ini menggunakan algoritma PSO untuk mencari solusi optimal dalam penjadwalan mata kuliah. Langkah-langkah dalam optimasi penjadwalan meliputi: Membentuk populasi partikel yang mewakili solusi potensial, Menetapkan jumlah partikel, kecepatan awal, koefisien kecepatan (c1, c2), dan faktor inersia, Mengembangkan fungsi tujuan yang mengevaluasi kualitas setiap solusi berdasarkan kriteria minimisasi konflik, optimalisasi penggunaan ruang, dan preferensi dosen serta mahasiswa, Memperbarui posisi dan kecepatan partikel berdasarkan rumus PSO, Mengevaluasi solusi baru, memperbarui pbest dan gbest berdasarkan hasil evaluasi, Menentukan kriteria konvergensi untuk mengakhiri iterasi algoritma. Dengan hasil penelitian ini, diharapkan dapat memberikan solusi yang praktis dan efektif bagi institusi pendidikan tinggi dalam mengelola penjadwalan mata kuliah, sehingga meningkatkan kualitas proses belajar mengajar.
Implementasi Algoritma Merkle Hellman untuk Keamanan Database Simanjuntak, Magdalena; Pasaribu, Tioria; Rahmadilla, Semiati
MEANS (Media Informasi Analisa dan Sistem) Volume 4 Nomor 1
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (339.35 KB) | DOI: 10.54367/means.v4i1.327

Abstract

The development of information technology today has a huge impact, namely the issue of security and confidentiality of data. One solution that can be used to guarantee the confidentiality and security of information is cryptography. By using cryptography, a data can be secured through the decryption and encryption process. Security issues and database confidentiality are the most important aspects of an information system. One mechanism to improve database security is to use asymmetric algorithms such as the Merkle Hellmen algorithm. Merkle Hellman is one of the crypto systems that uses the key type of asymmetry. In the Merkle Hellman system, the keys used are 2 different keys, namely the public key and the secret key. Encryption generates ciphertext and decryption produces a plaintext for securing databases that want to be kept confidential. The advantages of this Merkle Hellman algorithm is that there is no need for confidentiality in the key distribution process. From the results of experiments that have been done with this application, the encrypted database becomes a form of message that cannot be understood (ciphertext), but after the decryption process is done, the database is successfully returned to its original form (plaintext) that can be understood
Decision Support System for Choosing the Best Nurse Using the Multi Factor Evaluation Process (MFEP) Method at Djoelham Hospital, Binjai City Ningsih, Yulia; Simanjuntak, Magdalena; Saragih, Rusmin
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 01 (2024): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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

Abstract

Health workers are any person who is devoted to health and has knowledge and/or skills through education in the health field which for certain types requires the authority to carry out health efforts. A strategy is needed to increase the interest of health workers working in hospitals. The selection of exemplary health workers in hospitals is expected to be a motivation to increase the interest of health workers working in hospitals so that they can be a driver for the creation of health workers who have a nationalist, ethical and professional attitude, have a high spirit of service, are disciplined, creative, knowledgeable, skilled, virtuous and can uphold professional ethics. The purpose of this study is to evaluate the performance of nurses and reward the best nurses at Djoelham Hospital Binjai City. The use of the Multi Factor Evaluation Process (MFEP) method is relevant because it can help in integrating and evaluating various factors and criteria holistically. MFEP is a method that allows to evaluate various factors that affect decisions, as well as provide weight or value relative to each of these factors. The criteria used in this study are discipline, cooperation, loyalty, education, understanding of drug prescriptions, understanding of technology. The conclusion of this study is that the construction of this support system can help Djoelham Hospital in determining the best nurse and the use of the MFEP method in the decision support system to determine the best nurse increases accuracy in determining the best suitable nurse. This method is able to process various criteria that have been set, so that the results of decisions are more objective and fair compared to manual assessments.
Application of Decision Support System to Determine the Optimization of the Learning Plan Preparation Process in Schools Using the SAW Method Puspita Sari, Melani; Simanjuntak, Magdalena; Khadapi, Muammar
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 01 (2024): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

The preparation of an effective learning plan is one of the important factors in improving the quality of education. In the context of the 2024 Independent Curriculum, the flexibility and independence of schools in designing learning plans will be greater, but this also requires the right strategy in determining priorities and the resources needed. This research aims to develop a Decision Support System (DSS) that can help optimize the process of preparing lesson plans in schools using the Simple Additive Weighting (SAW) method. The SAW method was chosen because of its ability to assess and compare various alternatives based on predetermined criteria, such as Relevance to the World of Work, Project-Based Learning, Special Competency Development, Technology Utilization, Soft Skills Development, Plan Flexibility, Inclusive Learning, Collaboration with Industry, Critical Thinking Skills, Time Management. The results of this study show that the DSS implemented is able to provide more effective and efficient recommendations in preparing learning plans that are in accordance with the principles of the 2024 Independent Curriculum. Thus, it is hoped that this system can be a tool for educators in developing more structured and targeted learning plans.
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

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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.
A Decision Support System for the Selection and Distribution of Superior Durian Seedlings to the Community Using the Decision Tree Method Danisuwara, Ardiya Kansya; Manurung, Hotler; Simanjuntak, Magdalena
Pascal: Journal of Computer Science and Informatics Vol. 2 No. 02 (2025): Pascal: Journal of Computer Science and Informatics
Publisher : Devitara Innovations

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Abstract

The durian fruit is an agricultural commodity with high economic value and strong demand both domestically and internationally. However, the success rate of durian cultivation in Indonesia remains relatively low, at approximately 30.3%. This is partly due to the limited experience of farmers in managing durian plantations and the absence of an objective system for selecting eligible recipients of superior seedlings. Inaccurate selection of seedling recipients can lead to low productivity, suboptimal fruit quality, and an imbalance between market supply and demand. To address these issues, this study proposes the development of a Decision Support System (DSS) for the selection of superior durian seedling recipients using the Decision Tree algorithm. The study identifies several factors influencing eligibility, including age, land area, land ownership, farming experience, socioeconomic status, number of plants, water availability, membership in farmer groups, regional location, and education level. Data from 300 respondents were collected and processed through several preprocessing stages, including categorical data encoding, numerical data binning, normalization, and the division of training and testing datasets. The Decision Tree model was developed using the Scikit-learn library in the Python programming language, with the Gini index as the splitting criterion. The experimental results indicate that the model achieved an accuracy of 85%, a precision of 90%, and a recall of 95% for the "Eligible" class, demonstrating the system’s effectiveness in accurately identifying qualified recipients. The system was implemented as a GUI-based desktop application using Tkinter, equipped with features for data input, eligibility prediction, recipient data management, and statistical visualization. The implementation of this system is expected to enhance objectivity, efficiency, and accountability in the distribution of superior durian seedlings, thereby contributing to increased productivity among durian farmers and promoting better market equilibrium.
Co-Authors Achmad Fauzi Adek Maulidya Agus Sapitri, Liana Ambarita, Indah Ana, Putri Annatasia, Kristina Apriandi Alfa Reza Saragih Ariska, Dedek Arliana, Lina Athaya, Fara Buaton, Relita Danisuwara, Ardiya Kansya Defita Br. Manurung, Sella Fauzan Akbar, Ahmad Fauzi Ahmad Muda Fauzi, Achmad Ginting, Budi Serasi Hasibuan, Layla Mutiara Imran Lubis Kadim, Lina Arliana Nur Kahfi Lanang Khadapi, Muammar Khair, Husnul MANURUNG, HOTLER Maulidina, Nadia Melda Pita Uli Sitompul Mirah, Alta Muammar Khadafi Nopita, Siska Novriyenni Novriyenni, Novriyenni Nurhayati Nurhayati Pardede, Akim Manaor Hara Pasaribu, Tioria Pramana, I Gusti Puspita Sari, Melani Putra, Andika Cahya Rahayu, Nur Aprilia Rahimah Faizah Rahmadilla, Semiati Ratih Puspadini Roynaldi, Muhammad Rusmin Saragih, Rusmin Sari, Indah Kelana Satria R, Dandi Sawitri Sawitri Selfira, Selfira Sembiring, Arnes Sihombing, Anton Sinaga, Ayu Puspita Sari Syahputra, Siswan Tarigan, Kiki Dea Ananda Yani Maulita Yulia Ningsih Yunika , Vica