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All Journal Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 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 Ilmu Komputer dan Sistem Informasi (JIKOMSI) Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknologi Informasi dan Komunikasi 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 Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Languange, Linguistic, Communication & Media 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.
Penerapan Konsep Fuzzy dalam Mengamati Kualitas Pelayanan di Café Hitam Putih Dea Wiranti; Volvo Sihombing; Ibnu Rasyid Munthe
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 6 No. 1 (2023): Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Publisher : Utility Project Solution

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

Abstract

Penelitian ini bertujuan untuk menerapkan konsep fuzzy dalam mengamati kualitas pelayanan di Café Hitam Putih. Kualitas pelayanan yang baik di industri jasa seperti kafe memiliki dampak yang signifikan terhadap kepuasan pelanggan dan kesuksesan bisnis secara keseluruhan. Pendekatan fuzzy digunakan untuk mengatasi kompleksitas dan ketidakpastian dalam menilai kualitas pelayanan, mengingat banyaknya faktor subjektif yang terlibat dalam pengalaman pelanggan. Dalam penelitian ini, kerangka kerja fuzzy service quality diterapkan untuk mengukur dan menganalisis berbagai aspek kualitas pelayanan di Café Hitam Putih. Data dari survei pelanggan dan pengamatan lapangan digunakan untuk mengidentifikasi variabel-variabel penting yang berkontribusi pada persepsi pelanggan tentang kualitas pelayanan. Variabel-variabel ini kemudian dianalisis menggunakan logika fuzzy untuk menghasilkan nilai yang lebih akurat dan beragam dalam mengukur kualitas pelayanan. Hasil dari analisis fuzzy ini memberikan wawasan mendalam tentang aspek-aspek kualitas pelayanan yang perlu ditingkatkan dan diperbaiki di Café Hitam Putih. Penelitian ini memberikan panduan berharga bagi manajemen kafe dalam mengambil tindakan yang tepat untuk meningkatkan pengalaman pelanggan dan meningkatkan loyalitas pelanggan. Selain itu, pendekatan fuzzy juga menunjukkan potensi untuk diterapkan dalam evaluasi kualitas pelayanan di sektor jasa lainnya yang melibatkan tingkat subjektivitas yang tinggi. Dengan menerapkan konsep fuzzy dalam analisis kualitas pelayanan, penelitian ini memberikan kontribusi pada pemahaman yang lebih baik tentang bagaimana ketidakpastian dan kompleksitas dapat diatasi untuk mengukur dan meningkatkan kualitas pelayanan di industri jasa. Hasil-hasil ini dapat menjadi dasar untuk pengembangan strategi pelayanan yang lebih efektif dan peningkatan kepuasan pelanggan yang berkelanjutan.
Optimisasi Klasterisasi Nilai Ujian Nasional dengan Pendekatan Algoritma K-Means, Elbow, dan Silhouette Allbila Rahajeng Lashiyanti; Ibnu Rasyid Munthe; Fitri Aini Nasution
Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI) Vol. 6 No. 1 (2023): Jurnal Ilmu Komputer dan Sistem Informasi (JIKOMSI)
Publisher : Utility Project Solution

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

Abstract

Penelitian ini bertujuan untuk menerapkan algoritma K-Means dalam klasterisasi data nilai Ujian Nasional (UN) dengan pemanfaatan metode optimasi Elbow dan Silhouette. Klasterisasi data nilai UN memiliki potensi untuk mengidentifikasi pola yang ada dalam hasil ujian dan membantu dalam pemahaman lebih lanjut tentang karakteristik kelompok nilai yang berbeda. Dalam penelitian ini, kami menggunakan data nilai UN sebagai input untuk algoritma K-Means. Proses klasterisasi dilakukan dengan mempertimbangkan penggunaan metode optimasi Elbow dan Silhouette. Metode Elbow digunakan untuk menentukan jumlah klaster yang optimal, sementara metode Silhouette digunakan untuk mengevaluasi kualitas klaster yang terbentuk. Hasil penelitian ini menunjukkan bahwa penerapan algoritma K-Means dengan optimasi Elbow dan Silhouette dapat menghasilkan klaster yang relevan dari data nilai UN. Penentuan jumlah klaster menggunakan metode Elbow memberikan indikasi tentang jumlah kelompok nilai yang paling sesuai, sedangkan evaluasi menggunakan metode Silhouette membantu mengukur sejauh mana kelompok-kelompok tersebut terisolasi dan konsisten. Diharapkan bahwa hasil penelitian ini akan memberikan wawasan lebih lanjut tentang penggunaan algoritma K-Means dalam klasterisasi data nilai UN. Penemuan pola dalam klaster nilai UN dapat memberikan informasi berharga bagi lembaga pendidikan dan pengambil keputusan dalam mengembangkan strategi pendidikan yang lebih efektif. Dengan menggabungkan algoritma K-Means dengan metode optimasi Elbow dan Silhouette, penelitian ini memberikan kontribusi pada pemahaman kita tentang bagaimana teknik klasterisasi dapat diterapkan secara efektif dalam analisis data nilai Ujian Nasional. Selain itu, metodologi yang digunakan dalam penelitian ini dapat memiliki implikasi lebih luas dalam analisis data pada berbagai bidang lainnya
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): March 2023
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1375.967 KB) | DOI: 10.34288/jri.v5i2.205

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.
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.
Co-Authors Adam Wirayuda Ade Parlaungan Nasution, Ade Parlaungan Afrian Alfariz Aisyah Hayati Ali Akbar Ritonga Ali Akbar Ritonga Allbila Rahajeng Lashiyanti Angga Putra Juledi Arya Widana Bangun, Budianto Betaria Sonata Sianturi Bhakti Helvi Rambe Dahrul Aman Harahap Dea Wiranti Deci Irmayani Dermi Tinambunan Febriani Naibaho Feronika Feronika Fitri Aini Nasution Gandhi Naek Petromak Sinaga Gomal Juni Yanris Hanggi Kurniawan Inez Chania Panjaitan Iwan Purnama 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 Mushtafa Haris Munandar Mutiara Br Samosir Nur Rahma Fauziah Nurhikmah Nasution Nursaka Putra Rahel Marianti Ritonga Rahmadani Pane Ramadani Pane Ronal Watrianthos Sigit Prasetyo Nugroho Simanjorang, Elida F. S. Suci Ramadhani Syaiful Zuhri Harahap Volvo Sihombing Welfriede Siregar Windo Yenni Syahfutri Sipahutar Yoan Yoan Zulkifli