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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) RABIT: Jurnal Teknologi dan Sistem Informasi Univrab JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING JURNAL MEDIA INFORMATIKA BUDIDARMA JURTEKSI Minda Baharu Jurnal Riset Informatika Jurnal Mantik Informatika TRIDARMA: Pengabdian Kepada Masyarakat (PkM) Jurnal Tekinkom (Teknik Informasi dan Komputer) Jurnal Pengabdian Masyarakat Khatulistiwa Jurnal Teknologi Informasi dan Komunikasi Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan (JUSTIKPEN) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT
Ibnu Rasyid Munthe
Universitas Labuhanbatu
Aerospace Engineering Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Public Health Social Sciences Other

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Evaluation of Machine Learning Using The K-NN Algorithm to determine The Quality of Meat before consumption Feronika Feronika; Masrizal Masrizal; Ibnu Rasyid Munthe
Jurnal Riset Informatika Vol 5 No 2 (2023): Priode of March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v5i2.467

Abstract

Meat is one of the sources of animal protein for humans, and one of the requirements that must be met so that the human body does not lack protein, especially animal; this protein can be obtained from beef, chicken, and other meats, but the most important thing here is the content contained in meat, whether it has been contaminated with chemicals, e.g., chicken that has been injected with chemicals that cause the chicken to look fat, or beef whose flexibility has decreased and the pH is getting more acidic. This research tries to predict meat quality by looking at two parameters: flexibility and acidity. The programming language used is R Language, using the k-NN method or Algorithm to determine the meat's condition suitable for consumption. In detail, it will be processed in Machine Learning using the k-NN Algorithm; there are two criteria for consumption of meat, namely good or not good for consumption; in detail, the output will be explained using a specific graph using a plot function, and array data will be specifically classified to represent values. The value of 2 variables, namely feasible or not suitable for consumption.
ANALISIS MACHINE LEARNING ALGORITMA REGRESI LINEAR UNTUK MEMPREDIKSI SAHAM DI BANK BRI DI BURSA SAHAM INDONESIA Yenni Syahfutri Sipahutar; Ibnu Rasyid Munthe; Syaiful Zuhri Harahap
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 1 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i1.747

Abstract

Stocks are securities that have fluctuating characteristics. Therefore stock predictions are needed to determine stock prices in the future. The data used is actual data obtained from the Indonesian Stock Exchange. This study uses the CRISPDM model and uses the Linear Regression method in processing the data. Data processing is carried out using several techniques, namely manually (exel) and by application testing. The application used is Rapid Miner. And after testing, get the test results of a difference of 0 to 3%. And get a root mean square error (RMSE) value of 62.592. and based on the research, it was decided that the share price on January 4 2021 - December 9 2022 will experience stock price fluctuations in the future with a difference of 0 to 3% from the previous share price.
Fire Detection System At Labuhanbatu University Based On Internet Of Things (IoT) Iwan Purnama; Ibnu Rasyid Munthe; Khairul Khairul; Ronal Watrianthos; Zulkifli
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 4 (2023): August 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i4.4899

Abstract

Fire accidents are disasters that often occur compared to other fire disasters such as floods, landslides, earthquakes or tsunamis. Fires can occur at any time, and no one knows for sure when a fire accident will occur. The impact of a fire disaster is not only material that can disappear from human lives. The causative factors of fire disasters often occur due to human negligence and fires often occur in houses where the occupants have left them. Labuhanbatu University at night will be left by the owner and all lecturers and educational staff, only guarded by two security people with this condition, it is very dangerous when a fire occurs in one of the buildings. The purpose of this research is to focus on developing a fire detection system at Labuhanbatu University based on the Internet of Things to provide early warning of safety. The system uses three sensors, namely temperature sensor, gas sensor, and fire sensor. This research is R&D research using the ADDIE model with the following stages: analysis, design, development, implementation, and evaluation. The results of the fire sensor test were 90% successful, the results of the sensor test as soon as possible were 90% successful, and the results of the temperature sensor test were 90% successful. This fire detection system can minimize or minimize the occurrence of fire accidents and losses because it is based on the Internet of Things providing early information when a fire occurs to education staff and lecturers at Labuhanabtu University. Overall, this fire warning system can function properly.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PEMBERIAN PENGHARGAAN BAGI PELANGGAN TERBAIK MENGGUNAKAN METODE TOPSIS Dermi Tinambunan; Masrizal Masrizal; Ibnu Rasyid Munthe
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.732

Abstract

This study aims to solve the problem of awarding the best customers at HD Graphics companies. To make it easier for HD Graphics companies to choose the best customers to be awarded, a decision support system was built that can help make it easier for companies to select the best customers from that company. The decision support system was built using the Topsis method (Technique for Order Preference by Similarity to Ideal Solution). The criteria used consist of the percentage of customer purchases, the percentage of smooth payments by customers, customer loyalty, length of subscription, purchase intensity, and the number of cancellations by customers. The results of data processing from this research case study, obtained the 3 best customers, namely Customers 04, 06 and 01 with Vi values of 0.9478, 0.9077, and 0.8104.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN PELATIH KEGIATAN EKSTRAKURIKULER MENGGUNAKAN METODE MOOSRA Arya Widana; Volvo Sihombing; Ibnu Rasyid Munthe
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.1018

Abstract

This research aims to solve the problem of selecting extracurricular coaches. To assist the Faculty of Science and Technology at Labuhanbatu University in selecting trainers for extracurricular activities, a Decision Support System was designed using the MOOSRA (Multi-Objective Optimization based on Ratio Analysis) method. The Decision Support System (DSS) using the MOOSRA method was implemented to increase objectivity and efficiency in selecting trainers. Research methods include preliminary studies, determining criteria (experience, achievement, academics, skills, leadership), and data collection. MOOSRA is used to optimize decisions based on criteria. The ranking results show the three best coaches: Coach02, Coach08, and Coach07. The existence of this decision support system can help make it easier for the Faculty of Science and Technology, Labuhanbatu University, to select extracurricular trainers more quickly and efficiently so that they can support and increase effectiveness in supervising student extracurricular activities.
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN PRIORITAS PELATIHAN PENGGUNAAN ALAT PERTANIAN BERBASIS IOT DENGAN METODE ARAS Adam Wirayuda; Angga Putra Juledi; Ibnu Rasyid Munthe
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.1017

Abstract

The potential benefits of using IoT technology-based tools in agriculture are large, but implementation is still hampered by farmers' lack of understanding. Therefore, training was carried out taking into account the different conditions and needs of farmers in the Bagan Sinembah area. This research aims to build a Decision Support System (DSS) using the ARAS method in determining training priorities for using Internet of Things (IoT)-based agricultural equipment for farmers in the Bagan Sinembah area. Criteria for determining training priorities involve factors such as infrastructure availability, level of technological understanding, local topographic conditions, scale of agricultural business, and availability of funds and resources. The research results obtained consist of 3 groups of farmers who will receive the highest training priority, namely: alternative KTA8 in the first position with a result of 0.85821, alternative KTA4 in the second position with a result of 0.83197, and alternative KTA7 in the third position with a result 0.82643. The highest priority is given to farmer groups with the highest yields. The research results show that the system built can help make it easier for the Faculty of Science and Technology, Labuhanbatu University, to make decisions regarding training priorities for farmers. The results of this research can contribute to the development of a decision support system to increase the efficiency and effectiveness of farmer training in using IoT technology in agriculture in the Bagan Sinembah area.
SISTEM PENDUKUKUNG KEPUTUSAN PEMILIHAN SALON MOBIL TERBAIK DENGAN MENGGUNAKAN METODE WASPAS Afrian Alfariz; Ibnu Rasyid Munthe; Angga Putra Juledi
Jurnal Tekinkom (Teknik Informasi dan Komputer) Vol 6 No 2 (2023)
Publisher : Politeknik Bisnis Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37600/tekinkom.v6i2.697

Abstract

This research aims to build a Decision Support System (SPK) to choose the best car salon in the Rokan Hilir area. With increasing car ownership, the need for efficient maintenance has become crucial. The Weighted Aggregated Sum Product Assessment (WASPAS) method is used in this SPK. The research stages involve determining criteria, data collection, normalization, determining criteria weights, ranking alternatives, and evaluation. The criteria used in this research consist of price, quality, performance, technology and comfort. Of the nine alternatives, the ranking results show that SM04 is the best salon in the area. The final results of data processing using the WASPAS method in this study obtained 3 alternatives with the largest value, namely rank 1 alternative SM04 with a final result of 0.92715, rank 2 alternative SM02 with a value of 0.92448 and rank 3 alternative SM06 with a value of 0.92101. Through a decision support system for selecting the best car salon using the WASPAS method in the Rokan Hilir area, this design can make a positive contribution in helping vehicle owners make the best decisions, increase decision-making efficiency, and have a positive impact on the car salon industry in the Rokan Hilir area.
RANCANG BANGUN SISTEM INFORMASI GEOGRAFIS PEMETAAN HUTAN PADA KABUPATEN LABUHAN-BATU Ibnu Rasyid Munthe; Eliyas Wiko Wardana; Gomal Juni Yanris
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 6 No 2 (2021): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v6i2.1717

Abstract

The agricultural, plantation and forestry industries, which are the main choices of the population to meet household food needs and boost the community's economy, are very much in accordance with the geographical contours of Labuhanbatu Regency. A geographic information system that can provide position information, location coordinates, forest areas, forest information in Labuhanbatu Regency, and search paths for forest area locations. A web-based Geographical Information System (GIS) is required to determine the current position and location of forests. The waterfall method is used to build this GIS framework, which involves stages such as analysis, design, code generation, testing, and maintenance. MySQL is a database management system. PHP, Javascript, and HTML are used to create programming languages. Bootstrap user interface implementation. Black box testing is used to verify software. The test results show that the GIS meets the requirements and can solve system problems.
Pengembangan Sistem Informasi Prediktif Menggunakan Machine Learning Untuk Manajemen Risiko UMKM Dapot Hutagalung; Syaiful Zuhri Harahap; Ibnu Rasyid Munthe
Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan Vol. 5 No. 2.1 (2026): Jurnal Sistem Informasi, Teknik Informatika dan Teknologi Pendidikan
Publisher : Utiliti Project Solution

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

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) memiliki peran strategis dalam perekonomian nasional, namun masih menghadapi berbagai risiko seperti risiko keuangan, operasional, dan pasar yang dapat mengancam keberlangsungan usaha. Keterbatasan sumber daya dan kemampuan analisis risiko menyebabkan UMKM sulit melakukan mitigasi secara efektif. Penelitian ini bertujuan untuk mengembangkan sistem informasi prediktif berbasis machine learning yang dapat membantu UMKM dalam mengidentifikasi dan memprediksi risiko usaha secara lebih akurat. Metode yang digunakan meliputi pengumpulan data historis UMKM, pemodelan machine learning menggunakan algoritma klasifikasi dan regresi, serta evaluasi performa sistem berdasarkan tingkat akurasi, presisi, dan recall. Hasil penelitian menunjukkan bahwa sistem informasi prediktif yang dikembangkan mampu memberikan prediksi risiko yang cukup akurat dan dapat digunakan sebagai alat bantu pengambilan keputusan bagi pelaku UMKM. Dengan demikian, penerapan teknologi machine learning dalam manajemen risiko UMKM diharapkan dapat meningkatkan ketahanan dan keberlanjutan usaha
Forecasting IHSG Stock Prices Using an Attention-Based CNN-BiGRU Hybrid Deep Learning Ibnu Rasyid Munthe; Bhakti Helvi Rambe; Shabrina Rasyid Munthe
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 10 No 1 (2026): February 2026
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v10i1.7064

Abstract

This study develops an IHSG stock price forecasting model using a hybrid CNN–BiGRU architecture enhanced by an attention mechanism. The key novelty lies in combining CNN-based local pattern extraction with BiGRU-based bidirectional temporal modeling, while attention selectively emphasizes the most informative time steps, improving representation quality for complex and noisy financial series. Historical IHSG data from public sources were preprocessed through feature engineering and normalization, followed by XGBoost-based feature selection to retain the most predictive variables. Model robustness was assessed in two settings: (i) the full dataset and (ii) a “cleaned” dataset excluding the extreme COVID-19 volatility period. The proposed model achieved strong accuracy, with MAE/RMSE of 0.0125/0.02 on the full dataset and 0.0167/0.03 on the cleaned dataset, while Pearson correlation remained close to 1 in both scenarios, indicating high alignment with actual IHSG movements. A 30-day ahead forecast produced a stable and realistic trend. Overall, the CNN–BiGRU with attention provides an effective and robust approach for capturing multi-scale temporal patterns in IHSG forecasting.
Co-Authors Adam Wirayuda Ade Parlaungan Nasution, Ade Parlaungan Ade Trya Amanda Afrian Alfariz Aisyah Hayati Ali Akbar Ritonga Ali Akbar Ritonga Angga Putra Juledi Arnol Hamonangan Simbolon Arya Widana Bangun, Budianto Betaria Sonata Sianturi Bhakti Helvi Rambe Dahrul Aman Harahap Dapot Hutagalung Deci Irmayani Dermi Tinambunan Eliyas Wiko Wardana Fauziah Hanum Febriani Naibaho Feronika Feronika Gomal Juni Yanris Hanggi Kurniawan Inez Chania Panjaitan Iwan Purnama Kamsia Dorliana Sitanggang Khairul Kusmanto Kusmanto Lisma Zahara Marnis Nasution Marnis Nasution Masrizal Masrizal Masrizal Masrizal Masrizal Meidy Putra Panusunan Siregar Muhammad Halmi Dar Muhammad Syaifullah Muhammad Wahyu Tambunan Mulya Al Fazar Musthafha Haris Munandar Mutiara Br Samosir Novi Fitriandika Sari Nur Rahma Fauziah Nurhikmah Nasution Nursaka Putra Pane, Rahmadani Rahel Marianti Ritonga Ramadani Pane Ronal Watrianthos Shabrina Rasyid Munthe Sigit Prasetyo Nugroho Simanjorang, Elida F. S. Siti Hartati Yusida Saragih Syaiful Zuhri Harahap Volvo Sihombing Welfriede Siregar Windo Yenni Syahfutri Sipahutar Yoan Yoan Zulkifli