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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA International Journal of Health, Engineering and Technology Jurnal Penelitian Sistem Informasi Indonesian Journal of Education And Computer Science Jurnal Penelitian Teknologi Informasi dan Sains Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Modem : Jurnal Informatika dan Sains Teknologi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Marto Sihombing
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Decision Sciences, Operations Research & Management Dentistry Education Engineering Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Mechanical Engineering Medicine & Pharmacology Nursing Public Health Veterinary

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Jaringan Syaraf Tiruan Memprediksi Pemasangan Saluran Air Bersih Pengguna Baru Menggunakan Metode Backpropagation pada PDAM Tirta Sari Binjai Nanda Wahyudi Nasution; Magdalena Simanjuntak; Marto Sihombing
ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Vol 5, No 2 (2021): November 2021
Publisher : UIN Sumatera Utara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30829/algoritma.v5i2.10509

Abstract

Regional Drinking Water Company (PDAM) is a company engaged in clean water supply services, one of the goals of the establishment of PDAM is to meet the needs of the community for clean water, including the provision, development of facilities and infrastructure services, as well as clean water distribution. PDAM Tirta Sari Binjai as the person in charge of clean water needs in Binjai City installs water connection installations at customers' homes which usually come from the mountains then flows into rivers and is collected first in reservoirs and then distributed to customers' homes . PDAM Tirta Sari Binjai has experienced problems in handling the large number of applications for new connection installations. And to overcome these problems we need a method that can predict the number of new installations. Based on these conditions, PDAM Tirta Sari Binjai needs to create a system that can predict the number of installations of clean water lines that will come in the following days or months. The process of predicting the number of installations of clean water lines that will be carried out with a computerized system, one of the processes that can be done is the application of an Artificial Neural Network (ANN) using the Backpropagation method. The system is designed with the MATLAB R2014a programming application, after carrying out the data training process and data testing on 2016 to 2020 data, the learning rate is 0.2; the maximun epoch is 10000 and the target error is 0.001, the result is that in 2021 the number of installations of clean water channels is 2,238 channels. Keywords: Backpropagation, Artificial_Syaraf_Network, Clean_Air_Channel
Pengelompokan Tingkat Pengembangan Bakat pada Anak Menggunakan Metode Clustering Danianty Miranda Br. Bangun; Marto Sihombing; Victor Maruli Pakpahan
JURNAL PENELITIAN SISTEM INFORMASI (JPSI) Vol. 2 No. 3 (2024): Agustus : JURNAL PENELITIAN SISTEM INFORMASI
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jpsi.v2i3.2247

Abstract

Education is one measure of success or success in the knowledge/intelligence of students with their talents. Talent is an innate potential ability, which still needs to be honed and retrained so that it can become a capable ability, expertise and skills. Talent can be obtained from several ability tests that will be carried out so that children can find out what talents they have. MIS Mutia Rahma is a school that plays an important role in developing the talents of each of its students with various standards from the lowest to the highest. This school strives to maximize the potential of its students. Based on data obtained from 2010-2022, there are 1056 student talent development data. However, from this data it is not yet clear how the process of dividing groups and monitoring student talents is carried out. Therefore, an effective method is needed to process this data in order to classify the development of children's talents. The method that can be used to group data on children's talent development is the K-Means algorithm. Of the 20 data, there are 3 groups, namely group 1 has 7 data and group 2 has 11 data, and group 3 has 2 data with the most results obtained being cluster 2 with the group level of talent development in children aged (X) 8-10 years, who have the ability (Y) linguistic ability (KL), musical ability (KM), spatial ability (KS), kinesthetic ability (KK) & intrapersonal ability (KIP), the type of extracurricular (Z) that is developed in children is Football.
Pengelompokan Data Rekam Medis pada Pasien Penyakit dalam Untuk Meningkatkan Manajemen Informasi Kesehatan Berdasarkan Wilayah Kota Binjai Menggunakan Algoritma Clustering K- Means : (Studi Kasus: Artha Medika Binjai) Desiska Natalia Br. Purba; Marto Sihombing; Indah Ambarita
Jurnal Penelitian Teknologi Informasi dan Sains Vol. 2 No. 3 (2024): SEPTEMBER : JURNAL PENELITIAN TEKNOLOGI INFORMASI DAN SAINS
Publisher : Institut Teknologi dan Bisnis (ITB) Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54066/jptis.v2i3.2378

Abstract

The history of disease in patients is generally recorded in medical record data in every hospital as well as at Artha Medika Hospital which is a health institution that was established in 2012 in the city of Binjai also has a very large amount of medical record data. However, in using the information management system owned by Artha Medika Hospital, there are weaknesses and it is still limited in managing medical record data in the hospital which is used in making reports to the head of the leadership. Therefore, a system is needed that can assist the hospital in improving health information management to be faster in managing data by approaching using data mining techniques with the k-means method. So that in finding new information based on medical record data of internal medicine patients can be used in the decision-making process by hospital management to be right on target so that it can produce 3 groups of data consisting of Age, Type of disease and Region. From testing on cluster 3, it can be seen that the results of the age group (X), type of disease (Y), region (Z) the amount of data owned is 645 cluster 3 data centred on the centroid of the information of the number of patient medical records data, namely age is 44-52 years, with the type of disease is chronic kidney disease and the region is South Binjai.
Internet Of Things Based Milling Machine Design Using Esp8266 Nodemcu Hari sabana; Akim M. H. Pardede; Marto Sihombing
Indonesian Journal of Education And Computer Science Vol. 1 No. 2 (2023): INDOTECH - August 2023
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v1i2.42

Abstract

This study presents the design and development of a grinding machine based on the Internet of Things (IoT) utilizing NodeMCU ESP8266. The primary objective of this project is to integrate IoT technology into the grinding machine to enhance control, monitoring, and grinding process efficiency. By using NodeMCU ESP8266 as the microcontroller connected to a WiFi network, the grinding machine can be accessed and remotely controlled through devices connected to the internet. Users can access this platform to monitor the machine's condition and control it as needed. In this research, the focus is on circuit design, software development, and overall system integration. The testing results are performed by sending commands to start the DC motor, monitoring the DC motor's speed value, monitoring the grinding status, lid closure button, and reset button. The testing results indicate that this IoT-based grinding machine provides better control, real-time monitoring, and efficiency in the grinding process. Thus, this research portrays a successful implementation of IoT in the machinery industry, opening opportunities for further development in this field
Kandang Ayam Pintar Berbasis Internet of Thinks Menggunakan NodeMCU ESP8266 Muktashim Billah; Marto Sihombing; Rahmadani
Indonesian Journal of Education And Computer Science Vol. 2 No. 2 (2024): INDOTECH - August 2024
Publisher : PT. INOVASI TEKNOLOGI KOMPUTER

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.60076/indotech.v2i2.660

Abstract

Perancangan perangkat kandang ayam pintar berbasis IoT. Sistem perangkat ini menggunakan mikrokontroler NodeMCU ESP8266 yang berfungsi sebagai pengolah data dan pengirim perintah dan juga sebagai penerima jaringan WI-FI. Sistem perangkat kendang ayam pintar ini menggunakan system kontrol menggunakan smartphone android untuk mengontrol dan memonitoring keadaan, kendang ayam pintar ini menggunakan system komunikasi jaringan WI-FI sehingga sistem kendang ayam dan smartphone android dapat terhubung, dalam system kendang ayam pintar ini menggunakan sensor dht11 sebagai pengontrol suhu yang ada di dalam kendang ayam, dan motor servo sebagai penggerak penutup pakan ayam. Supply tegangan yang di gunakan oleh alat ini adalah AC-DC colokan listrik rumah untuk di hububgkan ke lampu, sumber tegangan yang di butuhkan pompa air 5volt. Tegangan AC-DC masuk terlebih dahulu ke rangkaian relay, 5 volt tengangan untuk menghidupkan pompa air. Alat ini dapat diguanakan dengan mudah untuk membantu kegiatan manusia dalam memelihara ayam hanya dengan menggunakan smartphone dan di hubungkan ke WI-FI.
Pemodelan K-Nearest Neighbor Untuk Identifikasi Pola Kepuasan Mahasiswa Terhadap Pelayanan Kampus (Studi Kasus : STMIK Kaputama) Muhammad Rizky R Ritonga; Marto Sihombing; Selfira Selfira
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 4 (2024): Oktober : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i4.238

Abstract

This research focuses on using the K-Nearest Neighbor (KNN) algorithm to model student satisfaction with campus services. The study finds that the quality of the dataset strongly influences the accuracy of the KNN classification results. Factors such as data cleanliness, balanced class distribution, and sufficient training data volume are highlighted as crucial for a successful model. The research also emphasizes the significance of proper feature selection in enhancing classification performance, suggesting that irrelevant features can introduce noise and decrease model accuracy. The model was evaluated using a dataset of 1032 data points and K=5, achieving an accuracy of 93.72%. While the model performed well for certain classes such as "Very Good" and "None", challenges were encountered in classifying the "Fair" and "Deficient" classes. The study concludes that KNN is effective in identifying student satisfaction patterns but highlights the need for improvements in accurately classifying these challenging classes. Ultimately, the research underscores the importance of data quality and feature selection in enhancing the performance of classification models for student satisfaction analysis.
Diagnosa Penyakit Tuber Culosis (TBC) menggunakan Metode Case Based Reasoning (CBR) : (Studi Kasus : RSUD Dr.R.M. Djoelham) Muhammad Reza Habibi; Rusmin Saragih; Marto Sihombing
Modem : Jurnal Informatika dan Sains Teknologi. Vol. 2 No. 4 (2024): Oktober : Modem : Jurnal Informatika dan Sains Teknologi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/modem.v2i4.212

Abstract

Tuberculosis (TB) is one of the infectious diseases caused by Mycobacterium tuberculosis bacteria infection in the human lungs. Tuberculosis is a disease that can be transmitted from people with TB through coughing, sneezing, talking, laughing or singing. Lack of public knowledge about TB and lack of funds for health checks make many people late to be treated. Expert systems are technologies developed based on programs, in accordance with human methods and mindsets. This aims to help people who want to check their health, but are hampered by costs, besides saving time if the examination place is far from the residential environment of the community concerned. Expert systems require a method that can help solve existing problems. In this study, the method used is the Case-Based Reasoning (CBR) method, because the main function of this method is to diagnose the disease. The calculation process of the Case-Based Reasoning (CBR) method which looks for the similarity value or proximity of old cases to new cases of a patient.
Diagnosa Penyakit Radang Sendi Menggunakan Metode Dempster Shafer William Jhonatan; Novriyenni Novriyenni; Marto Sihombing
Neptunus: Jurnal Ilmu Komputer Dan Teknologi Informasi Vol. 3 No. 3 (2025): Agustus : Neptunus : Jurnal Ilmu Komputer Dan Teknologi Informasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/neptunus.v3i3.1013

Abstract

Rapid technological advancements have brought convenience to various fields, including healthcare. Osteoarthritis (OA) is a chronic degenerative joint disease that often affects the knees and hips, particularly in the elderly, and is a major cause of pain, joint dysfunction, and reduced quality of life. The prevalence of OA increases with age, with risk factors such as obesity, excessive activity, and muscle weakness. Early and accurate diagnosis is essential for appropriate treatment. This study aims to develop a diagnostic system for inflammatory arthritis, specifically osteoarthritis, using the Dempster-Shafer method. This method was chosen because of its ability to combine various evidence and expert beliefs to produce a more accurate diagnosis. By utilizing mathematical proof theory, this system is expected to assist medical personnel in detecting OA symptoms more efficiently. The research findings are expected to contribute to the healthcare sector, particularly in improving the accuracy of osteoarthritis diagnosis, allowing for earlier and more appropriate treatment. This system can also be a supporting tool for doctors and patients in understanding joint health conditions.
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): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro 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.
Penerapan Metode Waspas dalam Pengambilan Keputusan Rekrutmen Anggota KPPS Pemilu Agung Aulia Tama; Marto Sihombing; Anton Sihombing
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 2 No. 4 (2024): Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/bridge.v2i4.223

Abstract

Members of the KPPS (Voting Organizing Group) are responsible for organizing voting in a polling station (TPS) during general elections in Indonesia. They are the spearhead in carrying out the democratization process by supervising and ensuring the continuity of elections honestly, fairly, and transparently. The duties of KPPS members include preparing TPS before voting begins, receiving and examining voters, supervising the election process to ensure compliance with applicable regulations, counting votes after voting is complete, reporting election results, and maintaining security and order around TPS. Decision support system is a Decision support system or Decision Support System (DSS) is an interactive system that supports decisions in the decision-making process through alternatives obtained from data processing results. The purpose of this study is to facilitate the recruitment of members of the Voting Organizing Group (KPPS). The research method is Weighted Aggregated Sum Product Assessment (WASPAS). WASPAS is to find the most appropriate priority location choices using weighting. The results of this study are that the development of this support system can help the KPU in selecting or selecting KPPS members and this decision support system as a tool in developing KPPS members by viewing or using criteria according to the criteria needed using the WASPAS method.
Co-Authors Agung Aulia Tama Ambarita, Indah Danianty Miranda Br. Bangun Desiska Natalia Br. Purba Dhany Fachrizal Hari sabana Imran Lubis Irfan Jaya Magdalena Simanjuntak Melda Pita Uli Sitompul Muhammad Reza Habibi Muhammad Rizky R Ritonga Nanda Wahyudi Nasution Novriyenni Novriyenni Pakpahan, Victor Maruli Pardede, Akim Manaor Hara Rahmadani RR. Ella Evrita Hestiandari Rusmin Saragih, Rusmin Selfira Selfira Sihombing, Anton Siswan Syahputra William Jhonatan Zian Sari