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All Journal Jurnal Sains dan Teknologi Techno.Com: Jurnal Teknologi Informasi Sistemasi: Jurnal Sistem Informasi JURNAL PENGABDI MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Jurnal Teknologi Informasi dan Multimedia Jurnal Bumigora Information Technology (BITe) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Jurnal Minfo Polgan (JMP) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA SWARNA Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) INCOME: Indonesian Journal of Community Service and Engagement Bakti Sekawan : Jurnal Pengabdian Masyarakat Jurnal Kecerdasan Buatan dan Teknologi Informasi Explore Prosiding Seminar Nasional CORISINDO
Syahrir, Moch.
Universitas Bumigora
Religion Agriculture, Biological Sciences & Forestry Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Energy Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Medicine & Pharmacology Social Sciences Other

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SHORTEST ROUTE SEARCH TO ACCOMMODATIONS NEAR MANDALIKA CIRCUIT USING DIJKSTRA'S ALGORITHM AND ANDROID-BASED LOCATION-BASED SERVICE Moch. Syahrir; Ahmad Subandi Azmi; Kurniadin Abd. Latif; Pahrul Irfan
Jurnal Kecerdasan Buatan dan Teknologi Informasi Vol. 5 No. 2 (2026): May 2026
Publisher : Ninety Media Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.69916/jkbti.v5i2.443

Abstract

The development of mobile technology, particularly on the Android platform, has created significant opportunities for real-time, location-based applications. One important implementation is the use of Location Based Service (LBS) in the tourism sector to help tourists efficiently find strategic locations. This study focuses on developing an Android-based LBS application that integrates the Dijkstra Algorithm to determine the shortest route to accommodations around the Mandalika Circuit area, Kuta Beach, Lombok, a leading destination for MotoGP events in Indonesia. The system development adopts the waterfall model, consisting of requirement analysis, system design, implementation, and testing. In the analysis phase, user needs related to accommodation information and route navigation are identified. The design phase includes system architecture, user interface, and digital map integration. Implementation is carried out by developing an Android application capable of accessing real-time location data and processing route calculations using the Dijkstra Algorithm to produce the most efficient path. The resulting application displays the distribution of nearby accommodations, provides travel distance information, and offers optimal route guidance that can be directly accessed by users. System testing shows that the application runs according to the defined functional requirements. Additionally, evaluation using a Likert-scale questionnaire indicates a user satisfaction level of 84%, reflecting good acceptance and usability. In conclusion, this research successfully implements LBS technology combined with the Dijkstra Algorithm in a mobile application, providing practical solutions for tourists visiting the Mandalika Circuit area.
Perbandingan Algoritma Sarima dan Prophet Untuk Peramalan Trend Penjualan Voucher Game Online M Rizki; Dadang Priyanto; Galih Hendro Martono; Neny Sulistianingsih; Moch Syahrir
Jurnal Minfo Polgan Vol. 14 No. 2 (2025): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v14i2.15083

Abstract

Industri game online terus mengalami perkembangan pesat, mendorong kebutuhan akan sistem peramalan yang akurat untuk mendukung pengambilan keputusan strategis dalam manajemen penjualan dan promosi. Studi ini bertujuan untuk membandingkan kinerja dua algoritma peramalan deret waktu, yaitu Seasonal Autoregressive Integrated Moving Average (SARIMA) dan Prophet, dalam memprediksi tren penjualan voucher game online di platform Kiyystore. Data yang digunakan dalam penelitian ini mencakup transaksi historis dari tahun 2022 hingga 2024, dengan total 5,530 data penjualan. Studi ini menerapkan metodologi Cross Industry Standard Process for Data Mining (CRISP DM) yang terdiri dari tahap pemahaman bisnis, pemrosesan data, pemodelan, dan evaluasi. Model SARIMA dipilih karena kemampuannya untuk menangkap pola musiman dan tren dalam data stasioner. Sementara itu, Prophet digunakan karena dirancang untuk menangani tren non-linear, pola musiman, dan anomali secara otomatis. Evaluasi kinerja dari kedua algoritma dilakukan menggunakan dua metrik utama, yaitu Mean Absolute Error (MAE) dan Root Mean Squared Error (RMSE). Hasil penelitian menunjukkan bahwa Prophet unggul dalam metrik MAE dengan nilai 0,7054, yang menunjukkan kinerja yang lebih baik dalam meminimalkan kesalahan rata-rata. Di sisi lain, SARIMA menunjukkan keunggulan dalam metrik RMSE dengan nilai 0,9514, yang berarti model ini lebih efektif dalam menangani kesalahan besar atau pencilan dalam prediksi. Studi ini memberikan kontribusi penting dalam pemilihan metode peramalan yang sesuai dengan karakteristik data. Dengan memahami keunggulan masing-masing algoritma, pelaku industri game online dapat lebih optimal dalam merencanakan strategi stok dan promosi, sehingga meningkatkan efisiensi dan daya saing bisnis secara keseluruhan
Pelatihan Seni Ilustrasi Berbasis Digital Sebagai Upaya Penguatan Kreativitas dan Pengembangan Potensi Industri Kreatif pada Generasi Muda di Kabupaten Lombok Barat Haryono Haryono; Irfan Hidayat; Moch. Syahrir; Gozin Najah Rusyada
SWARNA: Jurnal Pengabdian Kepada Masyarakat Vol. 5 No. 6 (2026): SWARNA : Jurnal Pengabdian Kepada Masyarakat, Juni, 2026
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi 45 Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/swarna.v5i6.3104

Abstract

Perkembangan teknologi digital telah membawa perubahan signifikan terhadap pola produksi dan distribusi karya seni visual, sehingga menuntut generasi muda untuk memiliki kompetensi kreatif yang relevan dengan kebutuhan industri kreatif modern. Namun demikian, masih terdapat keterbatasan pengetahuan dan keterampilan generasi muda di Kabupaten Lombok Barat dalam memanfaatkan teknologi digital sebagai media berkarya dan sarana pengembangan ekonomi kreatif. Oleh karena itu, kegiatan pengabdian kepada masyarakat ini bertujuan untuk meningkatkan kreativitas, keterampilan ilustrasi digital, serta pemahaman mengenai peluang industri kreatif melalui pelatihan seni ilustrasi berbasis digital. Metode pelaksanaan kegiatan dilakukan melalui beberapa tahapan, yaitu analisis kebutuhan peserta, pemberian materi konseptual, praktik pembuatan ilustrasi digital, pendampingan karya, dan evaluasi hasil pelatihan. Sasaran kegiatan adalah generasi muda yang memiliki minat pada bidang seni dan desain visual di Kabupaten Lombok Barat. Hasil kegiatan menunjukkan adanya peningkatan pemahaman peserta mengenai prinsip-prinsip ilustrasi digital, penguasaan perangkat lunak desain grafis, serta kemampuan menghasilkan karya visual yang kreatif dan bernilai ekonomis. Selain itu, peserta memperoleh wawasan mengenai peluang profesi dan kewirausahaan dalam sektor industri kreatif digital. Program ini memberikan kontribusi positif terhadap penguatan kapasitas sumber daya manusia kreatif daerah sekaligus mendukung pengembangan ekosistem industri kreatif berbasis teknologi digital di Kabupaten Lombok Barat.
Penerapan Metode Simple Additive Weigthed Dan Analitical Hierachy Proces Untuk Penentuan Dosen Penguji Skripsi Rian Maulana Rijaldi; Moch Syahrir
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 4 (2025): November
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i4.750

Abstract

The selection of thesis examiners is a crucial process for student academic success, yet it is often based on unstructured subjective considerations. This study aims to address this issue by designing and implementing a Decision Support System (DSS) to provide objective lecturer recommendations. The system integrates the Analytical Hierarchy Process (AHP) method for consistent criteria weighting and the Simple Additive Weighting (SAW) method to rank 10 lecturer alternatives based on four main criteria: Academic Qualification (K1), Time Availability (K2), Total Guidance (K3), and Structural Position Workload (K4). The AHP analysis results indicate that Academic Qualification (K1) is the highest priority criterion with a weight of 0.449 and a highly valid consistency ratio (CR = 0.04). Subsequently, the SAW calculation yielded three candidates who ranked at the top with a maximum preference score of 1.000. This study concludes that the hybrid AHP-SAW model provides an objective, transparent, and efficient framework for the lecturer selection process, successfully delivering accountable recommendations to assist decision-making within the academic environment.
Analisis Pola Pembelian Konsumen Menggunakan Algoritma FP-Growth pada Data Transaksi Restaurant Burger Nindya Alifia Khumaira; Dadang Priyanto; Hairani Hairani; Galih Hendro Martono; Moch. Syahrir; Husain Husain
Jurnal Teknologi Informasi dan Multimedia Vol. 8 No. 3 (2026): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v8i3.983

Abstract

Fast-food restaurants generate large volumes of transaction data that can be utilized to understand customer purchasing behavior and support business decision-making. However, transaction data are often used only for operational reporting, limiting their potential for identifying product association patterns. This study aims to apply the Frequent Pattern Growth (FP-Growth) algorithm to discover frequent itemsets and association rules from burger restaurant transaction data and implement the results in a web-based application. The dataset used consists of 2,001 burger restaurant transactions collected from Kaggle, covering the period 2021–2023. The research process included data preprocessing, transaction transformation, FP-Tree construction, frequent itemset extraction, and association rule generation using a minimum support threshold of 2 transactions and a minimum confidence threshold of 60%. The results revealed that the most frequent items were Save Point Sundae (191 transactions), Health Potion Smoothie (181 transactions), and Cheat Code Cookies (164 transactions). Several association rules achieved a confidence value of 100%, indicating a strong co-occurrence relationship between products. Furthermore, the rules Avatar Avocado -> Cosmic Rings and Cosmic Rings -> Avatar Avocado obtained a lift ratio of 1.50, demonstrating a positive association between the two items. These findings indicate that FP-Growth is effective in identifying customer purchasing patterns and can support promotional strategies, product bundling, and inventory management through data-driven decision-making.
Determination of the best rule-based analysis results from the comparison of the Fp-Growth, Apriori, and TPQ-Apriori Algorithms for recommendation systems Moch. Syahrir; Lalu Zazuli Azhar Mardedi
Matrix : Jurnal Manajemen Teknologi dan Informatika Vol. 13 No. 2 (2023): Matrix: Jurnal Manajemen Teknologi dan Informatika
Publisher : Unit Publikasi Ilmiah, P3M, Politeknik Negeri Bali

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31940/matrix.v13i2.52-67

Abstract

The popular association rule algorithms are Apriori and fp-growth; both of these algorithms are very familiar among data mining researchers; however, there are some weaknesses found in the association rule algorithm, including long dataset scans in the process of finding the frequency of the item set, using large memory, and the resulting rules being sometimes less than optimal. In this study, the authors made a comparison of the fp-growth, Apriori, and TPQ-Apriori algorithms to analyze the rule results of the three algorithms. TPQ- Apriori is an algorithm developed from the Apriori algorithm. For experiments, the Apriori and fp-growth algorithms use RapidMiner and Weka tools, while the TPQ-apriori algorithm uses self-built application programs. The dataset used is the sales data for the Kopegtel NTB department store, which has been uploaded on the Kaggle site. As for the results of testing the base rules from the overall results of testing the rules with the good Kopegtel dataset for 100%, 50%, and 25% of the total volume of the dataset, a conclusion can be drawn that the larger the dataset to be processed, the results will be more optimal when using the fp-growth algorithm RapidMiner, but not optimal if the dataset to be processed is small. It is different from using the Apriori and Weka FP-growth algorithms, where the resulting rules are less than optimal if the dataset used is large and optimal if the dataset is small. Several rules do not appear in the fp-growth and Apriori Weka algorithms because the two algorithms do not have a tolerance value in Weka's tools for the support of the rules that will be displayed. Meanwhile, the TPQ- Apriori algorithm that has been developed is capable of producing optimal rules for both large datasets and small datasets.
Model Deteksi Serangan Jaringan Menggunakan Machine Learning Dengan Teknik Ensemble Learning Lauw, christopher Michael; Advaita Hary, Adex; Anggrawan, Anthony; Syahrir, Moch.; Sulistianingsih, Neny
Jutisi : Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Vol. 15 No. 1 (2026): Februari 2026
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/jutisi.v15i1.3369

Abstract

Stock is one of the most popular investment instruments due to its potential to generate substantial returns. However, the high volatility of stock prices requires investors to employ accurate prediction models to support investment decision-making. This study aims to compare the performance of the Artificial Neural Network (ANN) and Support Vector Regression (SVR) methods in predicting the stock price of PT Gudang Garam Tbk using historical data enriched with technical indicators. The study adopted the Cross-Industry Standard Process for Data Mining (CRISP-DM) methodology, which consists of six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The prediction models were developed using historical stock price data enriched with technical indicators and evaluated using Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). The experimental results demonstrate that the ANN model outperformed the SVR model, achieving an MSE of 2923.86, RMSE of 1709.93, MAE of 1294.76, MAPE of 8.38%, and an R² of 0.68, while the SVR model obtained an MSE of 5211.06, RMSE of 2284.57, MAE of 2126.84, MAPE of 12.73%, and an R² of 0.42. Furthermore, the 240-day forecasting results indicate that the ANN model projects an upward (bullish) trend, whereas the SVR model predicts a relatively stable (sideways) trend. These findings indicate that the Artificial Neural Network (ANN) is more effective than Support Vector Regression (SVR) for predicting the stock price of PT Gudang Garam Tbk, as it produces lower prediction errors and demonstrates superior predictive performance. Keyword: Stock Price Prediction, Artificial Neural Network, Support Vector Regression, CRISP-DM. Abstrak Saham merupakan salah satu instrumen investasi yang banyak diminati karena berpotensi memberikan keuntungan yang tinggi. Namun, tingginya volatilitas harga saham menyebabkan investor memerlukan model prediksi yang akurat sebagai dasar pengambilan keputusan investasi. Penelitian ini bertujuan untuk membandingkan kinerja metode Artificial Neural Network (ANN) dan Support Vector Regression (SVR) dalam memprediksi harga saham PT Gudang Garam Tbk menggunakan data historis yang diperkaya dengan indikator teknikal. Penelitian ini menerapkan metodologi Cross-Industry Standard Process for Data Mining (CRISP-DM) yang meliputi tahapan business understanding, data understanding, data preparation, modeling, evaluation, dan deployment. Model dibangun menggunakan data historis harga saham yang diperkaya dengan indikator teknikal, kemudian dievaluasi menggunakan metrik Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), dan koefisien determinasi (R²). Hasil penelitian menunjukkan bahwa model ANN memberikan performa yang lebih baik dibandingkan SVR dengan nilai MSE sebesar 2923,86, RMSE sebesar 1709,93, MAE sebesar 1294,76, MAPE sebesar 8,38%, dan R² sebesar 0,68, sedangkan model SVR memperoleh nilai MSE sebesar 5211,06, RMSE sebesar 2284,57, MAE sebesar 2126,84, MAPE sebesar 12,73%, dan R² sebesar 0,42. Pada prediksi jangka panjang selama 240 hari, model ANN memproyeksikan tren harga yang meningkat (bullish), sedangkan model SVR menghasilkan tren yang relatif stabil (sideways). Berdasarkan hasil tersebut, dapat disimpulkan bahwa metode Artificial Neural Network (ANN) lebih efektif dibandingkan Support Vector Regression (SVR) dalam memprediksi harga saham PT Gudang Garam Tbk karena mampu menghasilkan tingkat kesalahan yang lebih rendah dan kemampuan prediksi yang lebih baik. Kata kunci: Prediksi harga saham; Artificial Neural Network; Support Vector Regression; CRISP-DM.
Co-Authors Advaita Hary, Adex Ahmad Ahmad Ahmad Subandi Azmi Ahmat Adil Andi Sofyan Anas Anthony Anggrawan Apriani Apriani Augustin, Kartarina Ayu Dasriani, Ni Gusti Azhar, Lalu Zazuli Azhar, Raisul Bukran Bukran Cahyablindar, Ayu Dadang Priyanto Darma, I Made Yadi Darmawan, I Made Angga Wahyu Fatimatuzzahra Fatimatuzzahra Galih Hendro Martono Gozin Najah Rusyada Hairani Hairani Hammad, Rifqi Hariyadi, I Putu Haryono Haryono Husain Husain I Nyoman Switrayana Irfan Hidayat Kartarina, Kartarina Kurniadin Abd Latif Kurniadin Abd. Latif lauw, Christopher Michael Lely Kurniawati Lilik Widyawati M Rizki M. Hidayatullah Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Melati Rosanensi Mudawil Qulub Muhamad Azwar Muhamad Azwar, Muhamad Muhammad Azwar Muhammad Zulfikri Muhammad Zulfikri Neny Sulistianingsih Neny Sulistianingsih Nindya Alifia Khumaira Ningsih, Neny Sulistia Ondi Asroni Pahrul Irfan Pahrul Irfan Ria Rismayati Rian Maulana Rijaldi Rifqi Hammad Riosatria, Riosatria Rismayanti, Ria Santoso, Heroe Sirojul Hadi Tri Sujaka, Tomi Wicaksono, Moh Arief Widayat, Windy Zulfikri, Muhammad Zulkipli Zulkipli