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All Journal Informatika Journal of Computer Science and Information Systems (JCoInS) IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT
Ibnu Rasyid Munthe
Sistem informasi, Fakultas Sains dan Teknologi, Universitas Labuhanbatu
Aerospace Engineering Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Environmental Science Library & Information Science Public Health Other

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Penyuluhan Membangun Wirausaha Di Desa Tanjung Siram Produk Sabun Cuci Piring Fauziah Hanum; Christine Herawati Limbong; Bhakti Helvi Rambe; Nur Ainun Gulo; Ibnu Rasyid Munthe; Syaiful Zuhri Harahap
JURNAL PKM IKA BINA EN PABOLO Vol 3, No 1: PENGABDIAN KEPADA MASYARAKAT | JANUARI 2023
Publisher : IKA BINA EN PABOLO : PENGABDIAN KEPADA MASYARAKAT

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/ikabinaenpabolo.v3i1.3746

Abstract

Kegiatan pengabdian masyarakat di Desa Tanjung Siram dilaksanakan pada tanggal 27 September 2022. Penyuluhan dan pelatihan ini berlangsung di aula kantor kepala desa Tanjung Siram. Peserta meliputi unsur masyarakat desa Tanjung Siram terkhusus kaum ibi-ibu, dosen dan mahasiswa. Kegiatan pengabdian masyarakat ini berjalan dengan baik dan lancar sesuai dengan yang direncanakan, hal ini terlihat dari antusias dan semangat wargaa dalam mengikuti pelatihan Pembuatan sabun pencuci piring. Tujuan dilakukannya pengabdian kepada masyrakat ini adalah untuk memberikan penyuluhan, pelatihan, dan praktek tentang pembuatan sabun pencuci piring dalam rangka membantu warga desa Tanjung Siram dalam menciptakan peluang usaha baru bagi warga serta mengurangi beban pengeluaran   warga dalam melakukan pembelian sabun pencuci piring.  Metode yang digunakan dalam pengabdian kepada masyarakat ini adalah dengan berdiskusi, memaparkan dan mempraktikkan cara pembuatan sabun pencuci piring.
Analisis Prediksi Jumlah Kunjungan Pasien di Puskesmas Aek Kota Batu Menggunakan Metode Regresi Linear Adinda Ayu Lestari Nasution; Marnis Nasution; Asriani Hasibuan; Ibnu Rasyid Munthe
Journal of Computer Science and Information System(JCoInS) Vol 7, No 3: JCoIns | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i3.9711

Abstract

Patient visit numbers at the Aek Kota Batu Community Health Center (Puskesmas) fluctuate over time; therefore, a method is required to predict visit volumes for future periods to inform healthcare service planning. This study aims to analyze historical patient visit patterns and develop a prediction model using linear regression. Monthly patient visit data from 2025 to 2026 were processed using the Knowledge Discovery in Databases (KDD) framework, comprising data selection, preprocessing, transformation, data mining, and evaluation. Analysis was conducted using POM-QM for Windows software, incorporating five independent variables based on time-series lags (lag-5 through lag-1). The results demonstrate that linear regression can generate a prediction model based on the relationship between historical data and future patient visit volumes. Model performance was evaluated using R², MAE, RMAE, MSE, RMSE, and MAPE metrics to assess prediction accuracy. The resulting model can serve as a decision-support tool for planning healthcare personnel, facilities, and services at the Aek Kota Batu Community Health Center, thereby enhancing the effectiveness and efficiency of service delivery.
Penerapan Algoritma Naïve Bayes Classifier dan Decision Tree untuk Memprediksi Tingkat Kepuasan Pelanggan Teras Coffe Rantauprapat Elfi Zahra Yuni; Syaiful Zuhri Harahap; Ibnu Rasyid Munthe; Angga Putra Juledi
Journal of Computer Science and Information System(JCoInS) Vol 7, No 3: JCoIns | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i3.9709

Abstract

The increasingly fierce competition in the coffee shop business requires business owners to understand customer satisfaction levels as a basis for improving service quality and maintaining customer loyalty. Therefore, an approach capable of accurately identifying and predicting customer satisfaction levels based on customer data is needed. Data mining is a data processing technique that can be used to discover patterns and important information from data sets to support the decision-making process. In this study, the Naïve Bayes Classifier and Decision Tree algorithms were used because both are classification methods capable of generating predictions based on the characteristics of the data. The research method used was a quantitative method by utilizing Teras Coffee Rantauprapat customer questionnaire data which was then processed using the Orange Data Mining application. The research data was divided into training data and testing data to build and test the classification models generated by both algorithms. The results showed that the Naïve Bayes and Decision Tree algorithms were able to classify customer satisfaction levels into satisfied and dissatisfied categories with a good level of accuracy. Based on the model evaluation results, the Naïve Bayes algorithm obtained superior performance compared to Decision Tree based on higher AUC, Precision, F1-Score, and MCC values. Thus, both algorithms can be applied to predict customer satisfaction levels, but Naïve Bayes proved more optimal in generating predictions on the dataset used in this study. The results of this study are expected to serve as a reference for Teras Coffee Rantauprapat in continuously improving service quality and customer satisfaction.
Analisis Data Penjualan Menggunakan Algoritma Apriori pada Analisis Kopi Tomi Hidayat; Ibnu Rasyid Munthe; Angga Putra Juledi
Jurnal Informatika Vol 12, No 3 (2024): INFORMATIKA
Publisher : Fakultas Sains & Teknologi, Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/informatika.v12i3.6064

Abstract

Data Mining is a technique for finding, searching, or extracting new information or knowledge from a very large set of data, by integration or merging with other disciplines such as statistics, artificial intelligence, and machine learning, making Data Mining as one of the tools to analyze data and then produce useful information. Association Rule is a process in Data Mining to determine all associative rules that meet the minimum requirements for support (minsup) and confidence (minconf) in a database. In Association Rule, there are 2 methods that can be used, namely a priori method and FP-Growth method, where FP-Growth method is the development of a priori method where a priori method there are still some shortcomings such as there are many patterns of data combinations that often appear (many frequent patterns), many types of items but low minimum support fulfillment, it takes quite a long time because database scanning is done repeatedly to get the ideal frequent pattern. In this study the method used is a priori algorithm method, a priori algorithm method is one of the alternative ways to find the most frequently appearing data sets (frequent itemset) without using candidate generation that is suitable for analyzing a transaction data. Coffee analysis is a Cafe Shop engaged in the sale of food and beverages that many food and beverage sales transactions. Open on November 7, 2021 coffee analysis penetrates 245 sales transactions and this transaction data continues to grow every day.
Rekayasa Fitur dan Gradient Boosting untuk Prediksi Harga Saham Pada Pasar Saham Indonesia Bhakti Helvi Rambe; Ibnu Rasyid Munthe; Fauziah Hanum; Anita Sri Rejeki Hutagaol
Journal of Computer Science and Information System(JCoInS) Vol 7, No 1: JCoInS | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i1.8945

Abstract

This study aims to analyze the comparative performance of three machine learning models Neural Network, Random Forest, and XGBoost in predicting the stock price of Bank Rakyat Indonesia (BBRI.JK) based on feature engineering integration. The background of this study is based on the need to develop accurate and efficient predictive models to deal with stock market volatility. The Data used covers the period 2010-2025 with the application of technical indicators such as Moving Average (MA), Relative Strength Index (RSI), volatility, and price momentum as the main features. The research method uses a machine learning approach based on supervised learning with a five-fold cross validation process. Model evaluation was conducted using quantitative metrics including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), coefficient of determination (R2), and Mean Absolute Percentage Error (MAPE). The results showed that XGBoost produced the Best Performance With R2 = 0.9451, MAE = 87.3129,and MSE = 10327.1187, followed by Random Forest (R2 = 0.9233) and Neural Network (R2 = 0.9120). The XGBoost Model proved to be the most stable and efficient in handling nonlinear data as well as extreme price fluctuations. The discussion confirms that the integration of engineering features improves the generalization capability of the model and lowers the prediction error rate significantly. Future research is recommended to include macroeconomic variables, sentiment data, and reinforcement learning approaches to broaden the scope and improve the model's adaptability to global financial market dynamics.
Klasifikasi Tingkat Stres Mahasiswa Dalam Penyelesaian Tugas Akhir Menggunakan Naïve Bayes Dan K-Nearest Neighbor Lenni Pefrianti; Ibnu Rasyid Munthe; Irmayanti Irmayanti; Masrizal Masrizal
Journal of Computer Science and Information System(JCoInS) Vol 7, No 1: JCoInS | 2026
Publisher : Universitas Labuhanbatu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36987/jcoins.v7i1.9060

Abstract

This study aims to analyze the stress levels of final-year students and compare the performance of Naïve Bayes and K-Nearest Neighbor (KNN) algorithms in stress classification. Data were collected from 82 respondents through a questionnaire consisting of seven variables (S1–S7) measuring factors contributing to stress, which were classified into low, moderate, and high stress levels. The results show that both algorithms can classify student stress effectively, with Naïve Bayes achieving the highest accuracy (90.15%) compared to KNN (87.72%). Distribution analysis by study program indicates that Agrotechnology has the highest proportion of students with high stress (42.86%), followed by Information Systems (40.63%) and Information Technology (13.64%). This study provides insights for the university to offer targeted support through counseling or stress management workshops.
Co-Authors Adinda Ayu Lestari Nasution Angga Putra Juledi Anita Sri Rejeki Hutagaol Asriani Hasibuan Bhakti Helvi Rambe Christine Herawati Limbong Elfi Zahra Yuni Fauziah Hanum Fauziah Hanum Irmayanti Irmayanti Lenni Pefrianti Marnis Nasution Masrizal Masrizal Nur Ainun Gulo Syaiful Zuhri Harahap Tomi Hidayat