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All Journal Jurnal Sistem Informasi Kaputama (JSIK) Journal of Artificial Intelligence and Engineering Applications (JAIEA) Nusantara Journal of Multidisciplinary Science Jurnal Ilmu Komputer dan Sistem Informasi Bridge: Jurnal Publikasi Sistem Informasi dan Telekomunikasi Repeater: Publikasi Teknik Informatika dan Jaringan Neptunus: Jurnal Ilmu Komputer dan Teknologi Informasi
Hermansyah Sembiring
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Religion Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Languange, Linguistic, Communication & Media Library & Information Science

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JARINGAN SYARAF TIRUAN MEMPREDIKSI KEBUTUHAN OBAT-OBATAN MENGGUNAKAN METODE BACKPROPAGATION M. Zulhamdany Bangun; Novryenni Novryenni; Hermansyah Sembiring
JSIK (Jurnal Sistem Informasi Kaputama) Vol 5, No 1 (2021): Volume 5, Nomor 1 Januari 2021
Publisher : STMIK KAPUTAMA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.1234/jsik.v5i1.454

Abstract

Health development is directed at increasing awareness, willingness and ability to live for everyone so that the highest public health status can be realized. In the current era of regional autonomy where health development is the responsibility of the regional government, the regions must be able to regulate themselves, one of which is in fulfilling their drug needs. To fulfill the need for medicine, good processing and planning are needed. One of the facilities or facilities needed for optimal health services to the community is the need for support in the form of drug availability for basic health services to suit their needs. Backpropagation is a multilayer Artificial Neural Network training because the backpropagation method has three layers in the training process, namely the input layer, hidden layer and output layer, where backpropagation is the development of a single layer network (Single Screen Network) which has two layers, namely the input layer and output layer. The drug data used were 2010 to 2019. With a maximum epoch of 0-10000, learning rate 0.1 and target errors ranging from 0.01 to 0.003 to produce convergent results. The results of the prediction of the number of drugs after carrying out the training process and testing have increased and decreased.
Design and Build a System for Turning on an IoT-Based Motorcycle Starter Using Voice Muhammad Alfanny; Achmad Fauzi; Hermansyah Sembiring
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 3 No. 1 (2023): October 2023
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v3i1.328

Abstract

Motorcycles require maintenance, especially on the engine, by always heating the motorcycle before use or when not in use. However, sometimes someone is lazy to warm up the motorcycle engine. The aim is to make it easier for users to warm up the motorcycle engine to avoid damage to the engine and make it easier for motorcycle users if the electric starter on the motorbike is damaged. Research using the IoT-based google assistant uses NodeMCU 8266 connected to an Android smartphone to start motorbikes automatically. Based on the research results this tool can function properly. The conclusion of the study is that the motorcycle starter system with a smartphone control system can avoid damage to the motorcycle engine, and makes it easier for users to start the motorcycle if the electric starter is damaged.
Sistem Pelaporan Kerusakan Jalan Raya Berbasis Android Dengan Metode Item Collaborative Filtering (Studi Kasus : Dinas PU Kota Binjai) Muhammad Prabowo Hartanta Sitepu; Yani Maulita; Hermansyah Sembiring
Nusantara Journal of Multidisciplinary Science Vol. 1 No. 2 (2023): NJMS - September 2023
Publisher : PT. Inovasi Teknologi Komputer

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

Abstract

Kerusakan jalan raya merupakan masalah yang umum terjadi di berbagai kota di seluruh dunia, termasuk Kota Binjai. Dalam upaya untuk mengatasi masalah ini, Dinas Pekerjaan Umum (PU) Kota Binjai membutuhkan sistem pelaporan kerusakan jalan yang efisien dan responsif. Penelitian ini mengusulkan pengembangan Sistem Pelaporan Kerusakan Jalan Raya Berbasis Android dengan menerapkan Metode Item Collaborative Filtering. Sistem ini dirancang untuk memungkinkan masyarakat secara mudah melaporkan kerusakan jalan raya melalui aplikasi Android yang dapat diunduh secara gratis. Metode Item Collaborative Filtering digunakan untuk mengelola laporan kerusakan jalan dan memberikan rekomendasi prioritas perbaikan berdasarkan histori laporan sebelumnya. Hal ini akan membantu Dinas PU Kota Binjai dalam mengalokasikan sumber daya dengan lebih efisien. Penelitian ini juga mencakup studi kasus pada Dinas PU Kota Binjai untuk menguji keefektifan sistem yang diusulkan. Hasil penelitian menunjukkan bahwa sistem ini dapat membantu dalam mendeteksi dan mengatasi kerusakan jalan raya dengan lebih cepat dan efisien, serta memberikan rekomendasi prioritas perbaikan yang lebih akurat. Dengan demikian, sistem ini memiliki potensi besar untuk meningkatkan layanan infrastruktur jalan raya di Kota Binjai dan berpotensi diadopsi oleh kota-kota lain dalam upaya meningkatkan kualitas infrastruktur jalan secara keseluruhan.
Penerapan Metode MOORA Dalam Pemilihan Produk Unggulan Daerah Pada Dinas Ketenagakerjaan dan Perindustrian Kota Binjai Indah Juliana; Achmad Fauzi; Hermansyah Sembiring
Jurnal Ilmu Komputer dan Sistem Informasi Vol. 1 No. 2 (2022): September 2022
Publisher : LKP Unity Academy

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70340/jirsi.v1i2.16

Abstract

MOORA method is a decision support system it is one that can perform calculations on attribute criteria that help decision makers to make the right decisions. Binjai City is an area that has several superior products. These products are superior and have characteristics and uniqueness that other regions do not have as well as reliable competitiveness and provide opportunities for employment opportunities for the surrounding community. To determine whether a product is included in the superior product category, this is determined by the Binjai City Manpower and Industry Office. There are several criteria that must be met, among others, labor, investment value, production capacity, production value, and BB/BP value. In this case the Department of Manpower and Industry of Binjai City finds it difficult to determine the superior product, this is because there are many criteria that must be considered. For this reason, a decision support system is needed that can help the Binjai City Manpower and Industry Office to determine regional superior products.
Penerapan IoT dalam Monitoring dan Pengendalian Kualitas Air Muhammad Yusri; Yani Maulita; Hermansyah Sembiring
Repeater : Publikasi Teknik Informatika dan Jaringan Vol. 2 No. 4 (2024): Oktober: Repeater : Publikasi Teknik Informatika dan Jaringan
Publisher : Asosiasi Riset Teknik Elektro dan Informatika Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62951/repeater.v2i4.250

Abstract

The decline in water quality is a serious environmental issue, particularly in urban areas. This research aims to develop an Internet of Things (IoT)-based water quality monitoring system using ESP32, pH, TDS, and turbidity sensors. The system is designed to monitor water quality parameters in real-time and transmit data to a cloud platform for further analysis. The system prototype was tested with water samples from various sources, and the results demonstrated high accuracy, with a maximum deviation of ±0.5% compared to laboratory results. Thus, this system offers an efficient and easy-to-implement solution for continuous water quality monitoring, which can aid in water resource management in urban environments.
Pengelompokkan Penyakit Tuberkulosis Paru Berdasarkan Penyebabnya Menggunakan Metode Clustering: Studi Kasus : UPT Puskesmas Selesai Cinta Apriliza; Relita Buaton; Hermansyah Sembiring
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.v3i3.995

Abstract

Pulmonary tuberculosis remains a pressing public health problem, particularly in the work area of the Duduk Health Center (UPT Puskesmas). Effective management of this disease requires a thorough understanding of the characteristics of the causes of pulmonary TB in patients. This study aims to classify pulmonary TB cases based on the main causes such as diabetes mellitus, irritant factors, pleural effusion, and family environmental conditions. The research method used is a clustering technique with the K-Means algorithm. The data used are data on pulmonary TB patients in 2020–2025 with variables of age, gender, and causative factors collected from medical records. The analysis process was carried out using MATLAB R2014b software. The clustering model was carried out in 3, 4, and 5 clusters to compare the level of segmentation efficiency. Based on the calculation results, the model with 5 clusters showed the lowest cluster variance value of 0.4889 compared to the 3-cluster model (0.7333) and 4-cluster models (0.6151), which indicates that the division into 5 clusters produces the most compact and representative data group. Each cluster shows a different combination of characteristics of pulmonary TB patients, for example: (1) elderly male patients with comorbid diabetes; (2) adolescent females with the negative influence of environmental factors; (3) adult males exposed to irritants; (4) patients with pleural effusion; and (5) groups with multiple factors. The results of this study can provide strategic input for the Finished Community Health Center UPT in formulating more targeted and targeted intervention policies in order to prevent, control, and handle pulmonary tuberculosis cases in a sustainable and effective manner.  
Diagnosa Penyakit Preeklamsia Menggunakan Metode Dempster Shafer : Studi Kasus : RSU Bidadari Sabina Eis Zulvahira Nasution; Novriyenni Novriyenni; Hermansyah Sembiring
Bridge : Jurnal Publikasi Sistem Informasi dan Telekomunikasi Vol. 3 No. 3 (2025): 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.v3i3.610

Abstract

Preeclampsia is one of the most serious complications in pregnancy, characterized by hypertension and proteinuria, and it poses a significant risk of maternal and fetal morbidity and mortality if not detected and managed promptly. Early detection is crucial, yet clinical diagnosis often faces challenges due to the variability of symptoms and uncertainty in medical decision-making. To address this issue, this study aims to develop an expert system for diagnosing preeclampsia by employing the Dempster-Shafer method, which is known for its ability to handle uncertainty and incomplete information in complex domains such as healthcare. A case study was conducted at Bidadari General Hospital, where data on clinical symptoms and patient medical records were collected and analyzed. The development process of the expert system followed systematic stages, including knowledge acquisition from obstetrics specialists, designing the knowledge base, constructing inference rules, and integrating the Dempster-Shafer algorithm for decision support. The system was subsequently tested using real-case scenarios of pregnant women suspected of having preeclampsia. Evaluation results demonstrated that the system achieved an accuracy rate of 92% in differentiating between preeclampsia and eclampsia, based on belief and plausibility measures combined with symptom analysis. These findings indicate that the proposed system can effectively support medical personnel by providing diagnostic recommendations with a high degree of reliability. In addition, the system offers efficiency in the clinical workflow by minimizing diagnostic errors and reducing delays in treatment initiation. Therefore, this expert system has the potential to become a valuable clinical decision support tool for early detection, risk assessment, and management of preeclampsia. Future development may focus on expanding the knowledge base, integrating real-time patient monitoring data, and enhancing usability to ensure broader applicability in diverse healthcare settings.
Co-Authors ACHMAD FAUZI Buaton, Relita Cinta Apriliza Indah Juliana M. Zulhamdany Bangun Muhammad Alfanny Muhammad Prabowo Hartanta Sitepu Muhammad Yusri Novriyenni Novriyenni Novryenni Novryenni Sabina Eis Zulvahira Nasution Yani Maulita