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All Journal ALGORITMA : JURNAL ILMU KOMPUTER DAN INFORMATIKA Jurnal Penelitian Sistem Informasi Indonesian Journal of Education And Computer Science Jurnal Penelitian Teknologi Informasi dan Sains Modem : Jurnal Informatika dan Sains Teknologi Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Marto Sihombing
Unknown Affiliation
Computer Science & IT Decision Sciences, Operations Research & Management Education

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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

Penelitian ini memaparkan rancangan dan pembangunan mesin giling berbasis Internet of Things (IoT) menggunakan NodeMCU ESP8266. Tujuan utama proyek ini untuk mengintegrasikan teknologi IoT dalam mesin giling guna meningkatkan kendali, pemantauan, dan efisiensi proses giling. Dengan menggunakan NodeMCU ESP8266 sebagai mikrokontroler yang terhubung dengan jaringan WiFi, mesin giling dapat diakses dan dikendalikan secara jarak jauh melalui perangkat yang terhubung ke internet. Pengguna dapat mengakses platform ini untuk memantau kondisi mesin dan mengontrolnya sesuai kebutuhan. Dalam penelitian ini, fokus diberikan pada desain rangkaian, pengembangan perangkat lunak, dan integrasi sistem secara keseluruhan. Hasil pengujian ini dilakukan dengan mengirimkan perintah motor dc hidup, monitoring nilai kecepatan motor dc, monitoring status giling, tombol penutup penampungan, dan tombol reset. Hasil pengujian menunjukkan bahwa mesin giling berbasis IoT ini mampu memberikan kontrol yang lebih baik, pemantauan real-time, dan efisiensi dalam proses penggilingan. Dengan demikian, penelitian ini menggambarkan penerapan IoT yang sukses dalam dunia industri mesin, membuka peluang untuk pengembangan lebih lanjut dalam bidang ini
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.
Co-Authors Ambarita, Indah Danianty Miranda Br. Bangun Desiska Natalia Br. Purba Hari sabana Magdalena Simanjuntak 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 William Jhonatan