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All Journal Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JPM (Jurnal Pemberdayaan Masyarakat) JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL PENGABDI JMM (Jurnal Masyarakat Mandiri) JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Teknologi Informasi dan Multimedia Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) REKA ELKOMIKA: Jurnal Pengabdian kepada Masyarakat Jurnal Teknik Informatika (JUTIF) Journal of Soft Computing Exploration Media Jurnal Informatika Jurnal Aplikasi Teknologi Informasi dan Manajemen (JATIM) ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal Interaktif: Warta Pengabdian Pendidikan (JIWPP) Jurnal Saintekom : Sains, Teknologi, Komputer dan Manajemen Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) MATRIX : JURNAL MANAJEMEN TEKNOLOGI DAN INFORMATIKA Paradigma Journal of Engineering, Technology and Computing (JETCom) INCOME: Indonesian Journal of Community Service and Engagement Insand Comtech : Information Science and Computer Technology Journal Journal of Artificial Intelligence and Digital Business Bakti Sekawan : Jurnal Pengabdian Masyarakat Journal Of Artificial Intelligence And Software Engineering Prosiding Seminar Nasional CORISINDO
Hammad, Rifqi
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Strategi Pelestarian Aksara Sasak melalui Mobile Game Edukatif Berbasis ADDIE Anas, Andi Sofyan; Tajuddin, Muhammad; Adil, Ahmat; Hammad, Rifqi
Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK) 2025: SNESTIK V
Publisher : Institut Teknologi Adhi Tama Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31284/p.snestik.2025.7354

Abstract

The Sasak script is an essential part of the cultural identity of the Sasak people in Lombok. However, interest among the younger generation in learning this script has been declining due to the dominance of the Indonesian language and globalization. Therefore, innovation in learning methods is necessary to ensure that the Sasak script remains preserved and appealing to students. One potential approach is gamification through educational mobile games. This study aims to develop a mobile game as a learning medium for the Sasak script using the ADDIE (Analysis, Design, Development, Implementation, and Evaluation) model. In the analysis phase, user needs and challenges in learning the Sasak script are identified. The design phase involves creating engaging gamification elements and an intuitive user interface. The development phase focuses on implementing technology to develop the mobile game. The game is then tested during the implementation phase, followed by the evaluation phase, which assesses its effectiveness in increasing students’ interest and understanding of the Sasak script, as well as evaluating the game’s content and interface. The results show that an ADDIE-based mobile game can enhance students’ motivation to learn the Sasak script. The use of gamification effectively creates a more interactive and enjoyable learning experience. Thus, this study contributes to the preservation of the Sasak script while providing an innovative solution for regional language education through digital technology.
Implementasi Kurikulum Merdeka Melalui Media Pembelajaran Berbasis Augmented Reality Matapelajaran Ilmu Pengetahuan Alam dan Sosial Hammad, Rifqi; Apriani; Muhid, Abdul
Jurnal Pemberdayaan Masyarakat Vol 10 No 1 (2025): Mei
Publisher : Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21067/jpm.v10i1.10730

Abstract

SLBN 2 Mataram is a special needs school with elementary, junior high and high school education levels. Currently the problem faced by SLBN 2 Mataram is the limited learning media for children with disabilities, the learning media used is still less attractive to students, the SLBN 2 Mataram teacher has never developed Augmented Reality-based learning media, the SLBN 2 Mataram school website has not seen the content and content.  Whereas making the content and content of the school website is very important for the existence of the organization's existence especially in the education environment. The solution offered from these problems is training in making Augmented Reality-based teaching media and training in making website content and content. There are several stages of activities carried out related to the solutions offered, starting from the stages of socialization, training, application of technology, mentoring and evaluation, and program sustainability. From the results of training activities obtained that there are 95.8% of teachers who can make Augmented Reality-based learning media and there are 90% of teachers who are able to manage and content the school website.
Optimizing Tourism Recommendations with a Hybrid Model: Bridging User Preferences and Behavioral Patterns Hammad, Rifqi; Azwar, Muhammad; Syarif, M. Aswin
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 4 (2025): August 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i4.6510

Abstract

Recommender systems play a crucial role in personalized decision-making, particularly in the tourism industry, where users seek destinations that align with their preferences. However, traditional recommendation methods often struggle to provide accurate recommendations. This study proposes a hybrid recommendation model that integrates Content-Based Filtering (CBF) and Apriori association rule mining to enhance recommendation quality. First, CBF was implemented using TF-IDF, Word2Vec, and BERT embeddings to compute the similarity between user preferences and tourism destinations. The Top-N recommended destinations from each method were then used as antecedents in Apriori to identify associative patterns and co-occurrence relationships among tourism destinations. By leveraging both semantic preference matching and association rule mining, the proposed system refines the recommendation process, ensuring not only personalized suggestions but also uncovering implicit travel patterns. The experimental results demonstrate that the hybrid model improves recommendation relevance and accuracy compared to standalone CBF methods. The accuracy of the CBF model was 53.96%, whereas that of the hybrid model was 94.31%. The integration of CBF and Apriori offers a more comprehensive and data-driven recommendation framework, which is valuable for personalized tourism applications.
Corn sales analysis using linear regression and svm methods to improve production planning Saputra, Ahmad Hakiki; Priyanto, Dadang; Hammad, Rifqi
Journal of Soft Computing Exploration Vol. 6 No. 3 (2025): September 2025
Publisher : SHM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52465/joscex.v6i3.591

Abstract

This research aimed to analyze and predict corn sales at UD Muara Kasih to improve production planning accuracy. The study used historical corn sales data collected over a specific period, covering 42 data entries from January 2021 to December 2024. The dataset included variables such as sales date, quantity sold, selling price per ton, total sales value, weather conditions, market demand (in tons), and the number of laborers. Prior to model training, the data underwent comprehensive preprocessing involving data cleaning, feature extraction, and normalization to ensure its quality and readiness for analysis. Two predictive models were applied: Linear Regression and Support Vector Machine (SVM) with a Radial Basis Function (RBF) kernel. Simulation data for 2024 and 2025 were generated based on the monthly averages derived from the historical dataset. The results showed that the Linear Regression model produced more stable predictions with a lower Root Mean Squared Error (RMSE) of 255.84 compared to the SVM model’s RMSE of 256.42. While the SVM model showed greater responsiveness to seasonal variations, the Linear Regression model was identified as the most suitable for the given dataset. The predictive models developed in this study are expected to support UD Muara Kasih in making more accurate and informed production decisions in the future.
Perancangan Aplikasi Kumpulan Sholawat Berbasis Multimedia Majelis Ta’lim As Salam Masbagik fatimatuzzahra, fatimatuzzahra; Hammad, Rifqi; Anas, Andi Sofyan; Azkari, Adzan Naufal
Jurnal SASAK : Desain Visual dan Komunikasi Vol. 5 No. 1 (2023): SASAK
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/sasak.v5i1.3027

Abstract

Penelitian ini bertujuan untuk mengembangkan aplikasi multimedia yang memudahkan jamaah dan masyarakat dalam mengamalkan ibadah sunnah, khususnya pembacaan Sholawat dan Maulid Addiya'ulami, tanpa mengabaikan ibadah wajib. Metode pengembangan yang digunakan adalah Multimedia Development Life Cycle, yang terdiri dari enam tahap: konsep, perancangan, pengumpulan bahan, pembuatan, pengujian, dan distribusi.Konsep pengembangan aplikasi ini mencakup identifikasi audiens, aturan dasar perancangan, dan spesifikasi arsitektur program. Tahap perancangan meliputi spesifikasi gaya, tampilan, dan kebutuhan materi. Selanjutnya, tahap pengumpulan bahan dilakukan untuk mengumpulkan semua materi yang dibutuhkan. Tahap pembuatan melibatkan rangkaian materi yang dirangkai berdasarkan desain dari storyboard dan struktur navigasi. Tahap pengujian dilakukan untuk memeriksa kesalahan setelah tahap pembuatan selesai. Tahap distribusi melibatkan implementasi aplikasi dan evaluasi. Hasil penelitian ini mencakup pemahaman tentang konsep Sholawat, jenis-jenis Sholawat, dan penggunaan bahasa pemrograman C# dalam pembuatan aplikasi. Pembahasan hasil penelitian didukung oleh kajian pustaka yang relevan. Selanjutnya, konsep aplikasi Sholawat ini dijelaskan dengan tujuan meningkatkan perkembangan Majelis Taklim dan Sholawat Assalam melalui aplikasi yang dapat diakses melalui perangkat smartphone Android.
Determining a Digital Marketing Strategy Using a Combination of Analytical Network Process (ANP) and Profile Matching Dakwah, Muhammad Mujahid; Roodhi, Mohammad Najib; Suprayetno, Djoko; Kusmayadi, Iwan; Abdurrahman, Abdurrahman; Hammad, Rifqi
Jurnal Bumigora Information Technology (BITe) Vol. 6 No. 1 (2024)
Publisher : Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/bite.v6i1.4125

Abstract

Background: Many MSMEs in the city of Matram are experiencing diculties in determining the digital marketing strategy to use. This is due to the many digital marketing strategies that can be used and the many factors that serve as criteria for selection.Objective: Develop a decision support system using a combination of ANP and profile-matching methods to assist MSMEs in determining the digital marketing strategy to be used.Methods: The method used in this research is a combination of ANP and Profile Matching Methods.Result: The combination of methods (ANP) and Profile Matching in determining digital marketing strategies has an accuracy of 83.33%.Conclusion: The combination of ANP and Profile Matching methods in determining digital marketing strategies has successfully recommended the best digital marketing strategy.
Data Augmentation-Driven Predictive Performance Refinement in Multi-Model Convolutional Neural Network for Cocoa Ripeness Prediction Apriani, Apriani; Switrayana, I Nyoman; Hammad, Rifqi; Irfan, Pahrul; Pratama, Gede Yogi
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 5 (2025): JUTIF Volume 6, Number 5, Oktober 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.5.5298

Abstract

Timely and accurate prediction of cocoa fruit ripeness is critical for optimizing harvest schedules, improving yield quality, and supporting post-harvest processing. Conventional visual inspection methods are prone to subjectivity and inconsistencies, especially when distinguishing among multiple ripeness levels based on fruit age. This study proposes a deep learning approach that leverages multi-model convolutional neural network transfer learning combined with image data augmentation to classify cocoa fruit into four maturity stages derived from fruit age. An augmented dataset of cocoa fruit images was used to fine-tune five well-established pre-trained models: MobileNetV2, Xception, ResNet50, DenseNet121, and DenseNet169. Data augmentation techniques were employed to increase variability and improve model generalization. Model evaluation was conducted using a standard 80:20 training-to-testing split to ensure sufficient data for learning while preserving a representative test set across all ripeness classes. The results demonstrate that DenseNet169 consistently outperformed other models, achieving the highest average accuracy of 85,05%, followed by DenseNet121 84,06%. Across all models, the use of data augmentation led to notable performance gains, highlighting its importance in enhancing predictive capability and reducing overfitting. The proposed framework shows promising potential for automating ripeness classification in agricultural contexts, offering a robust, scalable, and accurate solution for intelligent cocoa harvest management. This work contributes to the growing application of deep learning in precision agriculture, particularly in addressing fine-grained classification problems using limited but enriched visual data.
Selection of Outstanding Students Using AHP and Profile Matching Nasri, Muhammad Haris; Hammad, Rifqi; Irfan, Pahrul
Paradigma - Jurnal Komputer dan Informatika Vol. 26 No. 1 (2024): March 2024 Period
Publisher : LPPM Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/p.v26i1.3189

Abstract

The determination of outstanding students is the giving of awards to those who excel in academic and non-academic fields, aimed at motivating increased achievement. However, this process is often hampered by various criteria that must be considered, such as English language skills, work results, awards, and so on. The solution offered to overcome this problem is the development of a decision support system for selecting outstanding students using the AHP and Profile Matching methods. So, the aim of this research is to develop a decision support system for selecting outstanding students using a combination of the AHP and Profile matching methods, where later the system developed can assist decision makers in determining outstanding students. The results obtained from this research are a decision support system that uses 8 criteria and 26 alternative sample data which shows that "Mahasiswa F" is an outstanding student with a score of 4.09. The results of manual calculations with the system show similarities, which shows that the system developed is in accordance with expectations.
Speed Bump System Based on Vehicle Speed using Adaptive Background Subtraction with Haar Cascade Classifier Zulfikri, Muhammad; Kusuma, Wirajaya; Hadi, Sirojul; Husain, Husain; Hammad, Rifqi; Mardedi, Lalu Zazuli Azhar
Sistemasi: Jurnal Sistem Informasi Vol 13, No 3 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i3.3921

Abstract

Driving at high speed and recklessly is the main cause of traffic accidents. In several places speed bumps are installed as a medium to warn drivers to slow down the speed of the vehicle, but the installation of speed bumps in several places has become a problem in itself with inconvenience for drivers traveling at low speeds, so it is necessary to develop an intelligent system to warn drivers when speeding. vehicles break safety boundaries, making drivers safer and more comfortable. At the vehicle identification stage, a combination of the Adaptive Background Subtraction method with the Haar Cascade Classifier is proposed, and vehicle speed estimation is carried out by calculating the time difference in the detection area or Region of Interest (ROI). Testing was carried out using traffic videos with three conditions, namely day, evening and night, with each condition using the same object data, namely 55 images of car objects. The proposed method produces car detection accuracy with an average of 85% and MSE 0.5 in vehicle speed measurements.
Using a Partition System to Improve the Performance of the Apriori Algorithm in Speeding Up Itemset Frequency Search Process Syahrir, Moch; Hammad, Rifqi; Abd. Latif, Kurniadin; Rosanensi, Melati
Sistemasi: Jurnal Sistem Informasi Vol 13, No 1 (2024): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v13i1.3610

Abstract

The apriori algorithm uses minimum support and minimum confidence to determine appropriate itemset rules for decision making. The problem faced in this research is how to improve the performance of the a priori algorithm in the process of searching for itemset frequencies using data partition techniques, and be able to produce optimal and consistent rules. To overcome this problem, the author implemented the a priori method and partition system to improve the performance of the a priori algorithm for the itemset frequency search process by taking public data in the form of supermarket transaction data. In this research, the performance of the a priori algorithm was tested with and without a partition system. The data used in this research consists of 350 transaction data from 1784 records with a 4-itemset pattern, minimum support value of 20% and minimum confidence of 0.5 with the best standard rules for determining minimum confidence of 0.8. Based on this research carried out, the research results obtained are that for comparison of time and memory usage the apriori algorithm with a partition system is much faster than the apriori algorithm without a partition system, while memory usage is relatively less for the apriori algorithm with the system than the apriori algorithm without a partition system.
Co-Authors Abdul Muhid, Abdul Abdurahman Abdurrahman Ahmad Ahmad Ahmat Adil Al-Mu’min, Al-Mu’min Amrullah, Ahmad Zuli Andi Sofyan Anas Apriani Apriani Apriani Arfa, Muhammad Astuti, Emi Attaqwa, M.Aswin Syarif Azhar, Raisul Azhari, Anjas Ardiyan Azkari, Adzan Naufal Bukran Cahyablindar, Ayu Cokorda Javandira Fatimatuzahra, Fatimatuzahra Fatimatuzzahra Fatimatuzzahra Fatimatuzzahra Habib Ratu Perwira Negara Hairani Hairani Hardita, Veny Cahya Hariadi, Fadli Harisandi, Lalu Ilham Hidayat, Fadila Ananda Kartika Husain Husain Husaini, Rahayun Amrullah I Made Yadi Dharma I Nyoman Switrayana Ida Ayu Made Dwi Susanti Kartarina, Kartarina Kurniadin Abd Latif Kusmayadi, Iwan Lestari, I Desak Ayu Adhia M. Hidayatullah Mardedi, Lalu Zazuli Azhar Melati Rosanensi Miftahul Madani Muhammad Haris Nasri Muhammad Innuddin Muhammad Mujahid Dakwah Neny Sulistianingsih Nyoman Yudiarini Pahrul Irfan Panca Mukti, M Thoric Pratama, Gede Yogi Puspita Dewi, Puspita Qososyi, Sayidina Ahmadal Qulub, Mudawil rodhi, mohammad najib Roodhi, Mohammad Najid Samudra, Nanang Santoso, Heroe Saputra, Ahmad Hakiki Sembiring, Rinawati Sholeha, Eka Wahyu Sirojul Hadi Siswahyudianto Sujaka, Tomi Tri Sujaka, Tomy Tri Suprayetno, Djoko Suriyati ., Suriyati Syahrir, Moch. Syarif, M. Aswin Tajuddin, Muhammad Tanwir Tanwir Tri Sujaka, Tomi Wirajaya Kusuma Yuliana Yuliana Zamroni Alpian Muhtarom Zuhrian, Naufal Rifqi Zulfikri, Muhammad