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Eko Sumartono
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Jurnal Media Infotama
Published by Universitas Dehasen Bengkulu
ISSN : 18582680     EISSN : 27234673     DOI : https://doi.org/10.37676/jmi.v15i2.866
Core Subject : Science,
Computer Science & IT
Jurnal Media Infotama Fakultas Ilmu Komputer Universitas Dehasen Bengkulu memiliki ISSN: 1858-2680 dan e-ISSN : 2723-4673 merupakan jurnal ilmiah yang menerbitkan artikel ilmiah yang berhubungan dengan ilmu komputer dan ilmu yang berhubungan dengan komputer. Adapun topik artikel meliputi : Sistem Informasi, Teknologi Informasi, Pengolahan Citra, Robotika, Analisa Perangkat Keras dan Perangkat Lunak, Jaringan Komputer Data Mining, Clustering, Media Pembelajaran Berbasis Komputer, dan lain-lain. Jurnal Media infotama terbit dua kali dalam satu tahun, yaitu pada Bulan Februari dan September.
Arjuna Subject : Ilmu Komputer (semua kategori) - Ilmu Komputer (Lain-Lain)
Articles 544 Documents
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Identifikasi Tutupan Tata Guna Lahan Menggunakan Citra Satelit Di Desa Tanjung Baru Kecamatan Baturaja Timur Kabupaten OKU Azwar, Azwar; Lucyana, Lucyana; Meiwinda, Eka Rizki; Cahyo, Ali Mas’ud Dwi; Sapta, Andre Rayhan
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10544

Abstract

Land use changes occur from year to year. This information can be done periodically. There is a sensing technology used to describe objects and monitor changes in land use. The purpose of this study is to detect land use shifts in Tanjung Baru Village through satellite imagery. The data used is then analyzed through Land Use Analysis using ArcGIS Software with image maps from Google Earth. Tanjung Baru Village has an area of ​​442, 541 Ha. Tanjung Baru Village has areas such as residential areas with an area of ​​132, 532 ha, vacant land with an area of ​​75, 144 ha, plantations / forests with an area of ​​225, 050 ha and others with an area of ​​9,815 ha. The use of residential land is 30%, vacant land 17%, plantations / forests 51% and others 2%..
Pengembangan Prototype Aplikasi Mobile Pariwisata Berbasis MIT App Inventor untuk Mendukung Digitalisasi Destinasi Wisata Daerah Bandarsyah, Bandarsyah; Purnomo, Ari
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10886

Abstract

Digitalisasi sektor pariwisata kini menjadi tujuan strategis untuk memiliki aksesibilitas informasi yang lebih baik di berbagai destinasi wisata dan meningkatkan daya saing regional. Kabupaten Lampung Selatan memiliki potensi besar untuk pemanfaatan berkelanjutan sumber daya alam dan lautnya dalam pariwisata. Namun, hal ini belum didukung oleh sistem informasi mobile yang terintegrasi. Penelitian saat ini akan fokus pada pengembangan prototipe aplikasi pariwisata mobile yang dibangun di atas MIT App Inventor sebagai model awal untuk digitalisasi destinasi wisata regional. Pendekatan penelitian mengikuti metodologi tipe Research and Development (R&D) yang mencakup pembangunan model pengembangan prototipe yang didasarkan pada kombinasi analisis kebutuhan, desain sistem, pengembangan terapan, validasi ahli media, dan pengujian fungsional menggunakan metode Black Box Testing. Validasi dilakukan oleh dua kelompok ahli media dengan alat skala Likert. Tingkat kelayakan dalam hasil penelitian adalah 81,2% dengan kategori sangat layak. Pengujian fungsional menunjukkan bahwa semua fungsi bekerja sesuai desain. Prototipe ini menggambarkan bahwa alat teknologi pemrograman visual, seperti MIT App Inventor, dapat efisien dan efektif dalam memfasilitasi proses digitalisasi informasi destinasi wisata.
INTEGRASI APRIORI & FP-GROWTH PADA BUSINESS INTELLIGENCE SYSTEM UNTUK OPTIMALISASI STRATEGI PENJUALAN Lestari, Mita; Wijaya, Harma Oktafia Lingga
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10910

Abstract

Grocery stores as small-scale retail businesses generate large volumes of sales transaction data that have not been optimally utilized to support business decision-making. These transaction data contain valuable information in the form of customer purchasing patterns that can be analyzed using association rule mining techniques. This study aims to integrate the Apriori and FP-Growth methods into a Business Intelligence System to optimize sales strategies in a grocery store.Sales transaction data were processed using the WEKA application by applying two association rule mining methods, namely Apriori and FP-Growth. The Apriori method was implemented with a minimum confidence value of 90%, while the FP-Growth method used a minimum confidence value of 55%. The results show that the Apriori method produces association rules with a higher level of confidence, particularly for combinations of staple products such as rice, cooking oil, sugar, and instant noodles. Meanwhile, the FP-Growth method generates a wider variety of association rules with lower confidence values but offers superior computational efficiency in terms of processing time.A comparative analysis indicates that the Apriori method is more effective in producing highly reliable association rules for specific strategic recommendations, whereas the FP-Growth method is more suitable for exploring overall purchasing patterns with lower computational complexity. The integration of both methods into a Business Intelligence System provides strategic recommendations related to inventory management, product placement, and promotion planning based on customer purchasing behavior. Therefore, the proposed integration of Apriori and FP-Growth is expected to enhance sales strategy effectiveness and improve the competitiveness of grocery stores.
Analisis Hasil Imputasi Menggunakan Arsitektur Imputasi Autoencoder Kurniawansyah, Arius Satoni
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10940

Abstract

Missing values in multivariate time series data are a critical issue in many domains, especially in healthcare datasets such as MIMIC-IV. This study aims to analyze the performance of imputation results using an Autoencoder-based architecture. Autoencoder is a deep learning model capable of learning data representations and reconstructing missing values through latent feature extraction. The research methodology includes data preprocessing, missing value simulation, model training, and evaluation using metrics such as MAE, RMSE, and R². The results show that Autoencoder-based imputation provides competitive performance in reconstructing missing values, particularly in nonlinear and complex patterns. However, the model's performance depends on the proportion of missing data and network architecture design. This study contributes to understanding the effectiveness of Autoencoder in multivariate time series imputation and provides a baseline for further development using hybrid models.
ANALISIS PERBANDINGAN ALGORITMA DALAM MENEMUKAN POLA PEMBELIAN PRODUK PADA DATA PENJUALAN Asi, Antika dewi; Santoso, Budi; Daulay, Nelly Khairani; Wijaya, Harma Oktafia Oktafia Lingga
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10951

Abstract

The development of information technology in the Industry 4.0 era has driven significant changes in how organizations utilize data to support strategic decision-making. Data utilization is no longer limited to transaction recording but has shifted toward data processing as a source of predictive information that plays an important role in competitive business management. The urgency of this research is reinforced by the relatively low level of adoption of data-driven analytical systems among Micro, Small, and Medium Enterprises (MSMEs) in Indonesia, including the outdoor equipment rental sector. In the modern business environment, decision-making can no longer rely solely on intuition but must be supported by data and predictive analysis to improve efficiency and competitiveness. Therefore, the development of a Smart Inventory Management system based on Business Intelligence, implementing the Apriori and FP-Growth algorithms at SAVANA Outdoor Store, is expected to provide automatic recommendations for inventory requirements based on real and representative historical transaction patterns. Based on the results of processing outdoor equipment rental data using the Apriori algorithm with a confidence value of 68%, several association rules were obtained, indicating a tendency of dissimilar borrowing patterns (mutually exclusive relationships) among certain types of equipment. Meanwhile, the processing results using the FP-Growth algorithm demonstrated better performance. This algorithm successfully generated a total of 21 association rules, with the top ten rules having confidence values ranging from 70% to 72%.
ANALISIS POLA PEMILIHAN JALUR PENDAKIAN PENGUNJUNG DALAM MENDUKUNG BUSINESS INTELLIGENCE PARIWISATA Pitri, Mawar; Wijaya, Harma Oktafia Lingga
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10953

Abstract

This study aims to analyze visitor behavior patterns in selecting hiking trails at Mount as a basis for decision-making driven by Business Intelligence (BI). The primary issue addressed is the congestion of hikers on specific trails, which risks ecological degradation and diminishes the quality of the tourism experience. This research employs two main methods: Market Basket Analysis (Association Rules) to map hikers' equipment needs based on their chosen trails, and Spatial Zoning Analysis to manage visitor density distribution in real-time.The results indicate a strong association between the "Kawah Hidup" trail selection and the demand for masks, as well as night hiking trails with the rental of flashlights and heavy jackets. Based on these patterns, a service segmentation strategy was developed through four primary packages: "The Adventurer" (Jungle Trail), "The Express Sightseer" (Asphalt Trail), "Summit Explorer" (Gajah Peak & Dead Crater), and "Kawah Hidup Exclusive". Business Intelligence is implemented through digital information boards for zoning management and loyalty programs for local hikers to maintain the sustainability of jungle trails. The conclusion of this research confirms that optimizing logistics stock and digitalizing trail information enhances operational efficiency and supports the ecological sustainability of the Mount forest area.
Perancangan Dan Implementasi Sistem Keamanan Jaringan Menggunakan Alienvault Pada SMK N 4 Kota Bengkulu Lestari, Gita Dahlia; Jumadi, Juju; Asyhari, Ahmad
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10958

Abstract

This study aims to design and implement a network security system using AlienVault OSSIM at SMK N 4 Kota Bengkulu. The method used in this research is the Network Development Life Cycle (NDLC), which consists of analysis, design, simulation, implementation, monitoring, and management stages. AlienVault is implemented as a standalone operating system running on a virtual machine using VMware and is utilized to perform centralized network monitoring, threat detection, and security management. System testing is conducted using the black box method by simulating attacks such as port scanning and ping flooding. The test results show that AlienVault is capable of detecting network activities, identifying connected devices, and providing alerts for suspicious activities. With the implementation of this system, network security at SMK N 4 Kota Bengkulu becomes more controlled, secure, and supportive of teaching and learning activities.
Implemetasi Algoritma Binary Search Dalam Pengembangan Aplikasi Mobile Android Untuk Kamus Sinonim Dan Antonim Sari, Desi Novita; Maryaningsih, Maryaningsih; Akbar, Abdussalam Al
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10959

Abstract

This research aims to implement the Binary Search algorithm in the development of an Android mobile application for a dictionary of synonyms and antonyms to improve the efficiency of the data search process. The application development method used in this research is the Waterfall method, which includes requirements analysis, system design, implementation, testing, and maintenance. The Binary Search algorithm is applied to the search process for words that have been sorted alphabetically, thus speeding up search times compared to linear search methods. This application was developed using the Java/Kotlin programming language and a local database to store the collection of words along with their synonyms and antonyms. Test results show that implementing the Binary Search algorithm significantly increases word search speed and provides accurate results.
PREDIKSI HARGA SAHAM BANK MANDIRI BERDASARKAN DATA BMRI HISTORICAL STOCK PRICE Mahmud, Khairul Imam; Kurniawan, Rudi; Wijaya, Harma Oktafia Lingga
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.10983

Abstract

Stock price movements exhibit dynamic and highly fluctuating characteristics, making accurate prediction challenging when using conventional approaches. Therefore, this study aims to develop and evaluate a stock price prediction model for PT Bank Mandiri (Persero) Tbk using a deep learning approach based on Long Short-Term Memory (LSTM). The LSTM model is applied for time series forecasting by utilizing historical stock price data, with a primary focus on the closing price as the target variable. The historical stock price data of Bank Mandiri undergo preprocessing stages, including data cleaning and normalization using the Min–Max Scaling method, to align data scales and improve training stability and convergence. The proposed LSTM architecture consists of two LSTM layers with dropout mechanisms for regularization, followed by fully connected layers to generate stock price predictions. The model is trained using the Adam optimizer with Mean Squared Error (MSE) as the loss function. Model performance is evaluated using the Mean Absolute Percentage Error (MAPE) metric on the testing dataset. The experimental results show that the LSTM model achieves a MAPE value of 2.7572%, indicating a very high prediction accuracy. Furthermore, the future forecasting results suggest a relatively stable stock price movement with a gradual upward trend in the short term. Based on the findings, it can be concluded that the Long Short-Term Memory (LSTM) method is effective for predicting Bank Mandiri’s stock prices and has strong potential as a data-driven decision support tool for investment analysis, although it should be complemented with fundamental analysis and external market factors
Pengelompokan Data Penduduk Di Desa Penembang Menggunakan Algoritma K-Means Clustering Untuk Program Bantuan Sosial Santosa, Artha Dwika; Sari, Herlina Latipa; Prahasti, Prahasti
Jurnal Media Infotama Vol 22 No 1 (2026): April 2026
Publisher : UNIVED Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmi.v22i1.11031

Abstract

Population data clustering in Penembang Village using K-Means Clustering Algorithm-Means Clustering can help manage population data in Penembang Village, particularly in relation to social assistance. It can provide information on the results of population data grouping, which has been divided into two clusters, namely Cluster C1 (in dire need of assistance) and Cluster C2 (not in need of assistance). It can also help Penembang Village Office in determining the priority of residents who are in dire need of assistance so that social assistance programs are targeted appropriately. The desktop-based population data clustering application uses the Visual Basic.Net programming language with SQL Server database. Based on tests conducted using data from 12.5% of the total 241 households in 2024, namely 30 households, the results show that the group in dire need of assistance (Cluster C1) consists of 12 households with a percentage of 40%, and the group that does not need assistance (Cluster C2) consists of 18 households with a percentage of 60%. Based on testing of the program demo in Penembang Village Office, it is found that the population data grouping application is very easy to operate and very helpful in obtaining population data clustering information, thereby supporting the decision-making process in determining social assistance recipients in Penembang Village Office.

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