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Jurnal Komputer dan Teknologi (JUKOMTEK)
Published by Yayasan Pendidikan Cahaya Budaya Indonesia
ISSN : 29631289     EISSN : 29619009     DOI : https://doi.org/10.58290/jukomtek
Core Subject : Science,
Computer Science & IT Library & Information Science
Jurnal Komputer dan Teknologi (JUKOMTEK) e-ISSN 2961-9009 dan p-ISSN 2963-1289 merupakan jurnal ilmiah. Jurnal ini berisi tentang karya ilmiah bersifat open access, dan jurnal ilmiah nasional yang mempublikasikan artikel ilmiah hasil penelitian dalam ruang lingkup bidang ilmu komputer serta aplikasi informatika untuk pengembangan TIK. Frekuensi Terbit: 2 kali setahun (bulan Januari dan Juli).
Arjuna Subject : Ilmu Komputer (semua kategori) - Aplikasi Ilmu Komputer
Articles 93 Documents
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PENGEMBANGAN APLIKASI MANAJEMEN ARSIP BERBASIS FRAMEWORK LARAVEL DENGAN METODE PROTOTYPING Dian Yuthika Rizqi; Mohamad Facheri; Muhammad Reza Firmansyah Tanjung; Ahmad Fajar Sidiq
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.690

Abstract

The development of village archive management in Indonesia is still largely done manually, causing frequent damage, loss, and time-consuming searches. This study aims to develop a web-based village archive management application using the Laravel framework with the prototyping method. The background of this research is the suboptimal management of archives in Ngadas Village, Malang Regency. The objective is to build an application that can store, search, and secure village archives digitally. The method used is prototyping, consisting of four stages: communication, rapid design, user evaluation, and system refinement. Black-box testing and User Acceptance Test (UAT) were conducted with 5 village officials. The results showed that all functional features run as expected, and the user satisfaction level reached 86.4% (very feasible category). The conclusion is that the application effectively reduces archive search time from an average of 17 minutes to 12 seconds, improves data security, and fits the workflow of village apparatus
SIDITA: SISTEM INFORMASI DINAS PERTANIAN TERINTEGRASI BERBASIS WEB Beda Puspita Candra; Imam Thoib; Muhamad Sabil Shah Putra; Andoko Saifudin; Mochamad Dwi Andika; Fadhila Mu’afiyah; Ahmad Yusron Kafabi
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.699

Abstract

Agricultural data management in Nganjuk Regency has traditionally been conducted manually using physical records and separate spreadsheets, leading to inefficiencies, data duplication, and delays in reporting. To address these challenges, this study developed SIDITA, a web-based integrated agricultural information system designed to centralize and streamline data management across multiple sectors including crops, livestock, farmer institutions, and agricultural aid distribution. The objective of this research was to provide a practical solution that enhances efficiency, accuracy, and accessibility of agricultural data for the local government. The system was developed using the CodeIgniter framework, MySQL database, and responsive interfaces with Bootstrap and Tailwind CSS, following the waterfall development methodology. Testing was conducted using black box methods to ensure functional reliability. The results demonstrate that SIDITA successfully integrates diverse agricultural data into a single platform, reduces redundancy, and improves the speed and accuracy of reporting. In conclusion, the implementation of SIDITA supports better decision-making processes in agricultural management and provides a scalable model for digital transformation in local government institutions.
PREDIKSI HARGA SAHAM BANK NEGARA INDONESIA (BNI) BERDASARKAN HISTORICAL STOCK PRICE MENGGUNAKAN METODE GATED RECURRENT UNIT (GRU) Arina Kanuri; Elmayati Elmayati; Bunga Intan
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.705

Abstract

The volatile nature of stock price movements necessitates a prediction method capable of accurately modeling time-series data patterns. This study aims to predict the stock price of Bank Negara Indonesia (Persero) Tbk (BNI) using the Gated Recurrent Unit (GRU) method, based on historical data from the 2020–2025 period. The dataset includes closing prices, trading volume, moving averages (MA_7 and MA_21), volatility, and high-low ratios. The research process encompasses preprocessing, feature engineering, Min-Max normalization, the creation of sequential data with a time step of 30, and the splitting of data into training and testing sets. The GRU model is trained using the Adam optimizer, incorporating ReduceLROnPlateau and Early Stopping mechanisms to enhance training stability and mitigate the risk of overfitting. Model performance is evaluated using MSE, RMSE, MAE, R², and MAPE metrics. The results demonstrate that the GRU model yields effective predictions, achieving a MAPE of 3.91%, an RMSE of 211.21, and an R² of 0.746. Stock price predictions for the upcoming 10 days indicate a trend of gradual price decline. These findings suggest that the GRU method is effective for predicting BNI stock prices based on historical data and holds potential for supporting investment decision-making, while acknowledging external factors that influence market conditions.
SISTEM PERAMALAN PENJUALAN KOPI BUBUK SELANGIT MENGGUNAKAN METODE WEIGHTED MOVING AVERAGE (WMA) MENGGUNAKAN DATA TIME SERIES BERBASIS FRAMEORK CI (CODEIGNITER) Glen Jupiter; Armanto Armanto; Nelly Khairani Daulay
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.706

Abstract

This study aims to forecast ground coffee sales using the Weighted Moving Average (WMA) method to support decision-making in business planning. The WMA method was selected because it assigns greater weight to recent historical data, thereby enabling a more responsive capture of changes in sales trends. The results indicate that ground coffee sales are projected to experience a stable upward trend in 2025. The model achieved a high level of accuracy, yielding a MAPE of 0.098%, an MAE of 54.463, and an RMSE of 70.683. Although discrepancies occurred in certain periods due to high sales volatility, the WMA method generally tracked actual data patterns effectively and produced realistic estimates. Consequently, the WMA method is a suitable tool for sales forecasting to support production planning, inventory control, and the formulation of more effective sales strategies.
ANALISIS SENTIMEN ULASAN SHOPEE DI PLAY STORE DENGAN NAIVE BAYES: Sentiment Analysis of Shopee Reviews on the Play Store Using Naive Bayes Muh Arfah Wahlil Pratama; Suriyadi Suriyadi; Nurjaya Nurjaya; Haldi Alfaisal; Melni Melni; Andi Nurwafia; Nurkhafitri Sahra; Asya Syara Marzan; Arman Rusdin; Dirgasari Dirgasari
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.710

Abstract

This study evaluates user sentiments towards the Shopee application on the Google Play Store using a dataset of 1,000 crawled reviews. Preprocessing methods including cleaning, normalization, stopword filtering, and stemming were conducted, followed by feature weighting using TF-IDF. The classification model based on the Naive Bayes Classifier yielded an overall accuracy of 84.5%, with a precision of 85.2%, recall of 83.8%, and F1-score of 84.5%. The analysis suggests that shipping promotions are the main drivers of positive sentiment, while post-update app performance drops and payment errors trigger negative reviews.
RANCANG BANGUN ALAT PENGUNCI PINTU OTOMATIS BERBASIS PENGENALAN WAJAH MENGGUNAKAN RASPBERRY PI siti Ayu Luffiyati Hamid; Ahwan Ahmadi; Hadian Mandala Putra; Hendra Setiawan; Ida Nurcahyani
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.715

Abstract

Door security systems in government institutions generally still rely on conventional locks, which have several limitations, including the risk of being lost, easily duplicated, and the inability to automatically verify user identities. These limitations allow unauthorized visitors or individuals to enter restricted rooms without an authentication process, potentially disrupting office activities. This study aims to design and develop an automatic door locking system based on facial recognition using a Raspberry Pi as an alternative security solution. An experimental research method was employed by integrating a Raspberry Pi 3 Model B+, a USB camera, a 5V relay module, and a solenoid door lock as the hardware components, while the software was developed using the Python programming language, the OpenCV library, and the Local Binary Pattern Histogram (LBPH) algorithm for facial recognition. The proposed system detects a user's face and compares it with the registered facial database before automatically unlocking the door for authorized users. Experimental results indicate that the system achieved a facial recognition accuracy ranging from 40% to 90%, with performance influenced by lighting conditions, facial orientation, and the use of accessories such as eyeglasses. In addition, the system successfully controlled the automatic door locking mechanism according to the authentication results. Therefore, the developed system can serve as a practical and effective alternative to conventional door locking systems for improving access control in office environments
ANALISIS PENGARUH SELEKSI FITUR TERHADAP KINERJA RANDOM FOREST PADA KLASIFIKASI KUALITAS UDARA onesimus Harefa; Septian Trio Sitohang; Glenn Desmon Sirait; Rado Rama Jaya Manurung; Jaya Tata Hardinata
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.717

Abstract

One of the important indicators that must be monitored to reduce the effects of pollution on health and the environment is air quality. Using the Beijing Multi-Site Air Quality dataset, this study investigated the influence of feature selection on the performance of the Random Forest algorithm in air quality classification. Orange Data Mining is used to process data through the stages of preprocessing, feature selection, model formation, and 10-Fold Cross Validation. The results of the feature selection resulted in five main attributes: PM10, CO, NO₂, SO₂, and O₃. The Random forest algorithm yielded an accuracy of 76.6%, AUC of 0.948, accuracy of 0.764, recognition of 0.766, F1 score of 0.764, and MCC of 0.689. The results show that feature selection has succeeded in simplifying the model by reducing the number of attributes, but it has not been able to improve the performance of Random Forest compared to the use of all attributes.
PERANCANGAN SISTEM INFORMASI MANAJEMEN LOGISTIK OBAT MASUK DAN OBAT KELUAR Iqbal Dzulfiqar Isakandar; Nazhua Puput Syaharani; Iqbal Dzulfiqar Iskandar; Imam Amirulloh
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.719

Abstract

PT AAN is a pharmaceutical distribution company that still relies on a manual record-keeping system using physical logbooks and Excel files. This practice has led to a high risk of data inaccuracies, shipping delays, and difficulties in real-time inventory tracking. This study aims to design a web-based Monitoring System for Incoming and Outgoing Drug Distribution to enhance efficiency, accuracy, and real-time oversight in the company's pharmaceutical logistics management. The system was developed using a prototyping approach, which encompasses requirement analysis, conceptual design using Unified Modeling Language (UML) modeling, prototype construction, system evaluation, and functionality testing using the Black Box Testing method. The design results demonstrate that the proposed system successfully maps two main user roles (Admin and Warehouse Staff) with features for automatic stock validation and data redundancy elimination, while achieving a 100% success rate in interface functionality testing via the Black Box method. In conclusion, the implementation of this web-based monitoring system design provides a significant contribution by minimizing human errors, accelerating distribution workflows, and optimizing the accuracy of month-end stock reporting at PT AAN.
KOMPARASI PERFORMA ALGORITMA K-NEAREST NEIGHBOR DAN SUPPORT VECTOR MACHINE UNTUK KLASIFIKASI CITRA TEKSTUR TENUN budiman baso
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.723

Abstract

The diversity of woven fabrics on Timor Island makes it difficult to distinguish between types of woven fabrics and their origins. Each region on Timor Island has its own woven fabric motifs that represent local culture. There are Timor woven motifs that look similar but have different types. Each motif and process of making weaving on Timor Island can describe the type and origin of the weaving. To distinguish Timor woven fabrics, it can be seen from the style of motifs or textures contained in Timor woven fabrics. Therefore, the pattern recognition of Timor woven motifs with the concept of classification is implemented using the K-Nearest Neighbor (KNN) and Support Vector Machine (SVM) classification algorithms based on texture feature extraction using the Gray Level Co-Occurrence Matrix (GLCM). This study aims to compare the performance of the two classification algorithms. Several parameters are used to configure the KNN and SVM algorithms to determine the performance gap between the two algorithms. The experimental architecture was carried out on 500 images of Timor weaving with 4 motifs; the image dataset will be divided into 75% training data and 25% testing data using the hold-out validation method. From the experimental results, the KNN algorithm using Euclidean distance with 1 Neighbor obtained the best performance, with an Accuracy rate of 88.73%, Precision of 89.41%, Recall of 88.73%, and F1-Score of 89.07%.
ANALISIS SENTIMEN ULASAN PENGGUNA APLIKASI PINTEREST PADA GOOGLE PLAY STORE MENGGUNAKAN ALGORITMA NAIVE BAYES CLASSIFIER Muh Arfah Wahlil Pratama; Suryadi Suryadi; Haldi Alfaisal; Nurjaya Nurjaya; Nia rahmadani Nia; Ahmad Ashar; Elsa Elsa; Riksal Rivaldi; Sri Rejeki; Dimas Rezkianto; Iin Sugiarti; Zulfikar Isnansah
Jurnal Komputer dan Teknologi Vol 5 No 2 (2026): JUKOMTEK JULI 2026
Publisher : Yayasan Pendidikan Cahaya Budaya Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.64626/jukomtek.v5i2.728

Abstract

The rapid growth of user reviews on the Google Play Store offers valuable insights for app developers. This study conducts sentiment analysis on Pinterest user reviews using the Naive Bayes Classifier algorithm. Data was collected via web scraping using the `google-play-scraper` library within the Google Colaboratory environment. The dataset, consisting of Indonesian-language reviews, underwent preprocessing steps including case folding, tokenization, stopword removal, and stemming. Feature extraction was performed using TF-IDF, and the model was evaluated using a confusion matrix with an 80:20 train-test split. The results indicate that the Naive Bayes model achieved an accuracy of 84.00%, a precision of 84.00%, and a recall of 77.78%. The sentiment distribution reveals a predominance of positive reviews, reflecting overall user satisfaction with the Pinterest app. This research contributes to the understanding of public opinion regarding visual-based applications and validates the effectiveness of Google Colab as an integrated platform for Indonesian-language sentiment analysis.

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