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All Journal Sistemasi: Jurnal Sistem Informasi JOURNAL OF APPLIED INFORMATICS AND COMPUTING JURNAL PENGABDI JISICOM (Journal of Information System, Infomatics and Computing) 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) 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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Religion Arts Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Social Sciences Other

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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.
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.
Klasterisasi Pemain PUBG Mobile dengan Algoritma K-Modes Clustering pada Mayoung Universe Harisandi, Lalu Ilham; Sujaka, Tomi Tri; Hammad, Rifqi
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5520

Abstract

Mayoung Universe merupakan penyelenggara turnamen Esport dan Leader komunitas yang aktif mengadakan turnamen PUBG Mobile di wilayah Nusa Tenggara Barat. Dari sekian banyak turnamen yang telah di selenggarakan akan tetapi Mayoung Universe mengalami kesulitan dalam menentukan pola penyelenggaraan turnamen PUBG Mobile untuk kedepannya, Selain itu, Terdapat data pendaftaran pemain berjumlah 401 data, serta didominasi oleh atribut yang bertipe kategorik Oleh sebab itu, menerapkan algoritma K-Modes Clustering sangatlah cocok di terapkan untuk mengatasi permasalahan serta untuk menangani data kategorik. Algoritma K-Modes merupakan pengembangan dari K-Means yang dirancang untuk mengelompokkan data kategorikal Hasil evaluasi dari Elbow Method terdapat jumlah k optimal yakni 4,6 dan 8, Hasil evaluasi dari Silhouette Coefficient jumlah k terbaik sebanyak k=6, dengan skor=0,249. Sedangkan evaluasi dari Dunn index dan DBI, k terbaik terdapat pada k=3, skor dari Dunn index yaitu 0,643, dan skor DBI yakni skor 2,395. Hasil dari visualisasi jumlah k=6 dan 3, terdapat jumlah objek terbanyak pada cluster 1 sebanyak 197 untuk k=6, dan 231 untuk k=3. Untuk mengatasi permasalahan bagaimana menentukan pola turnamen PUBG Mobile di Nusa Tenggara Barat, pihak Mayoung Universe hendaknya memperhatikan cluster 1 dari 3 dan 6 jumlah k terbaik dengan segala bentuk karakteristiknya. Sebagai gambaran, dalam menyelenggarakan turnamen PUBG Mobile kedepannya. 
Perbandingan Algoritma XGBoost dan Random Forest dalam Klasifikasi Surat Masuk Pemerintahan Hidayat, Fadila Ananda Kartika; Sulistianingsih, Neny; Hammad, Rifqi
RIGGS: Journal of Artificial Intelligence and Digital Business Vol. 4 No. 4 (2026): November - January
Publisher : Prodi Bisnis Digital Universitas Pahlawan Tuanku Tambusai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31004/riggs.v4i4.5044

Abstract

Pengelolaan surat masuk pada lingkungan pemerintahan daerah berperan penting dalam mendukung efektivitas administrasi dan pengambilan keputusan oleh pimpinan daerah. Surat masuk merupakan salah satu bentuk komunikasi resmi yang harus dikelola secara sistematis agar informasi yang terkandung di dalamnya dapat segera ditindaklanjuti sesuai dengan bidang administrasi terkait. Volume surat yang tinggi sering menimbulkan berbagai kendala, terutama dalam proses identifikasi isi surat dan pengelompokan berdasarkan bidang administrasi yang berwenang. Kondisi tersebut berpotensi menyebabkan keterlambatan disposisi, kesalahan pengelompokan surat, serta menurunnya kualitas pelayanan administrasi apabila masih dilakukan secara manual. Penelitian ini bertujuan untuk membandingkan kinerja dua algoritma machine learning, yaitu Extreme Gradient Boosting (XGBoost) dan Random Forest, dalam melakukan klasifikasi surat masuk kepala daerah secara otomatis dan terstruktur. Data yang digunakan dalam penelitian ini meliputi arsip surat masuk pemerintah daerah periode 2021–2022 serta hasil ekstraksi dokumen surat berbentuk PDF yang diperoleh dari aplikasi persuratan SRIKANDI menggunakan pustaka pdfplumber untuk menghasilkan data teks yang dapat diolah.. Tahapan penelitian mencakup proses pra-pemprosesan data, pembagian data menjadi data latih dan data uji, pelatihan model, serta evaluasi kinerja model menggunakan indikator accuracy, precision, recall, dan F1-score. Berdasarkan hasil pengujian, algoritma XGBoost menunjukkan performa yang lebih unggul dengan nilai akurasi sebesar 81,87% dan F1-score 82,00%, dibandingkan Random Forest yang hanya mencapai akurasi 76,00% dan F1-score 76,03%. Dengan demikian, XGBoost dinilai lebih efektif untuk mendukung proses klasifikasi surat dalam implementasi e-government di lingkungan pemerintahan daerah.
Optimalisasi Pemanfaatan Platform Digital dalam Merancang Identitas Produk dan Pemasaran Digital Madu Trigona Lebah Prawira Desa Sokong Lombok Utara Tri Sujaka, Tomi; Abd Latif, Kurniadin; Syahrir, Moch; Hammad, Rifqi; Bukran
INCOME: Indonesian Journal of Community Service and Engagement Vol 4 No 4 (2025)
Publisher : EDUPEDIA Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56855/income.v4i4.1907

Abstract

Kelompok Ternak Madu Trigona Lebah Prawira merupakan kelompok ternak yang ada pada wilayah Desa Sokong Kecamatan Tanjung Kabupaten Lombok Utara. Permasalahan utama yang dihadapi mitra meliputi rendahnya pemanfaatan media digital, belum terbentuknya identitas merek yang kuat, serta keterbatasan pengetahuan dan keterampilan dalam pemasaran digital. Kegiatan ini bertujuan untuk mengoptimalkan pemanfaatan platform digital dalam mendukung branding dan pemasaran produk madu trigona. Metode Pelaksanaan kegiatan meliputi sosialisasi, pelatihan digital branding dan pemasaran digital, penerapan solusi pemasaran digital, dan pendampingan serta evaluasi. Hasil kegiatan menunjukkan adanya peningkatan pemahaman dan keterampilan mitra dalam membangun identitas merek, menghasilkan konten promosi yang lebih menarik dan konsisten, serta memanfaatkan media sosial dan marketplace untuk memperluas jangkauan pemasaran. Pembahasan menunjukkan bahwa penerapan strategi branding dan pemasaran digital mampu meningkatkan visibilitas produk, interaksi dengan konsumen, serta potensi peningkatan nilai jual madu trigona. Optimalisasi pemanfaatan platform digital melalui pendekatan pelatihan dan pendampingan terbukti efektif dalam meningkatkan daya saing dan keberlanjutan usaha madu trigona di era digital.
Rice Leaf Disease Classification Based on ResNet50 and MobileNetV3 Feature Extraction Using Random Forest Pratama, Gede Yogi; Husaini, Rahayun Amrullah; Nasri, Muhammad Haris; Hammad, Rifqi
Media Jurnal Informatika Vol 17, No 2 (2025): Media Jurnal Informatika
Publisher : Teknik Informatika Universitas Suryakancana Cianjur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35194/mji.v17i2.5939

Abstract

Diseases in rice plants are one of the main factors contributing to decreased agricultural productivity. Early and accurate disease identification is crucial to support effective decision-making in plant disease management. This study aims to compare the performance of deep learning models based on Convolutional Neural Networks (CNN), namely ResNet50 and MobileNetV3, as well as their integration with the Random Forest (RF) algorithm for rice leaf disease classification. The dataset used consists of rice leaf images categorized into several disease classes. Model performance was evaluated using accuracy, precision, recall, and F1-score metrics with a macro-average approach. The results show that the standalone ResNet50 and MobileNetV3 models achieved accuracies of 62.5% and 65.7%, respectively, with macro F1-scores below 0.65, indicating moderate classification performance. However, combining CNN models with Random Forest significantly improved classification performance. The ResNet50 + RF model achieved an accuracy of 99.6%, while the MobileNetV3 + RF model attained the highest accuracy of 99.8%, along with equally high macro-averaged precision, recall, and F1-score values. These findings demonstrate that integrating CNN-extracted features with the Random Forest algorithm enhances the model’s ability to distinguish disease classes more accurately and consistently. Therefore, the hybrid CNN–Random Forest approach shows strong potential as an effective solution for image-based rice plant disease detection systems.
Intervensi Edukasi Digital Marketing untuk Peningkatan Pengetahuan Siswa Siswi Madrasah Aliyah Nasri, Muhammad Haris; Hammad, Rifqi; Husaini, Rahayun Amrullah; Roodhi, Mohammad Najid; Pratama, Gede Yogi
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol. 6 No. 1 (2026): Juni
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v6i1.913

Abstract

Advances in digital technology require young people to possess adequate digital literacy skills, particularly in digital marketing, which is now a crucial skill in education, the workplace, and entrepreneurship. However, observations indicate that Madrasah Aliyah (MA) students still lack a grasp of digital marketing concepts and practices. This community service activity aims to improve MA students' knowledge and understanding of basic digital marketing concepts, social media promotion strategies, digital branding principles, and consumer behavior in the digital world. The activity is divided into three stages: preparation, implementation, and evaluation. The preparation phase includes initial discussions with schools and the development of training materials. During the implementation phase, materials are delivered through interactive lectures and discussions, followed by simple practices using digital platforms. Assessments were conducted using pre- and post-tests to measure student knowledge gains. The results showed significant improvement, with an average pre-test score of 42.7 rising to 82.4 in the post-test. This 39.7-point increase indicates that the training successfully strengthened students' understanding of digital marketing concepts. This activity is effective in improving MA students' digital literacy and is relevant to continue with mentoring to better prepare them to face the challenges of the digital era
Design and Implementation of a Music Bot and Its Impact on the Community of the Discord Server Slowly Hariadi, Fadli; Husain, Husain; Hammad, Rifqi
Journal of Engineering, Technology and Computing (JETCom) Vol. 4 No. 3 (2025): Journal of Engineering, Technology and Computing (JETCom)
Publisher : Yayasan Bina Internusa Mabarindo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.63893/jetcom.v4i3.307

Abstract

This study aims to design, implement, and evaluate a music bot integrated into the Discord platform, as well as to analyze its impact on community interactions within the “Slowly” Discord server. The bot was developed using the Python programming language and the discord.py library, and tested through black-box testing to ensure the reliability of its core features such as play, pause, resume, skip, queue, and disconnect. The findings indicate that the bot operates stably and meets user expectations. In terms of usage, the bot contributes to increased interaction within the community. The average number of users in the voice channel increased from 1–3 to 4–7 users per day, while the average session duration grew from 2–3 hours to 3–5 hours. Furthermore, during one week of observation, a total of 58 songs were played, demonstrating that the bot not only serves as an entertainment tool but also strengthens social engagement within the community. This study recommends further development including the addition of comprehensive documentation (help command), performance optimization to reduce delays, periodic evaluation of user needs, and migration to cloud hosting services to ensure system availability. Thus, the Discord music bot has the potential to become an integral part of enhancing social interaction and user experience in online communities
Co-Authors Abdurahman Abdurrahman Ahmad Ahmad Ahmat Adil Al-Mu’min, Al-Mu’min Andi Sofyan Anas Apriani Apriani Arfa, Muhammad Astuti, Emi Attaqwa, M.Aswin Syarif Azhar, Raisul Azhari, Anjas Ardiyan Azkari, Adzan Naufal Bukran Bukran Cahyablindar, Ayu Fatimatuzahra, Fatimatuzahra 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 Kartarina, Kartarina Kurniadin Abd Latif Kusmayadi, Iwan Lestari, I Desak Ayu Adhia M. Hidayatullah Mardedi, Lalu Zazuli Azhar Melati Rosanensi Miftahul Madani Mohammad Najib Roodhi Mudawil Qulub Muhammad Haris Nasri Muhammad Innuddin Muhammad Mujahid Dakwah Neny Sulistianingsih Pahrul Irfan Pratama, Gede Yogi Puspita Dewi, Puspita Qososyi, Sayidina Ahmadal Roodhi, Mohammad Najid Santoso, Heroe Saputra, Ahmad Hakiki Sholeha, Eka Wahyu Sirojul Hadi Sujaka, Tomi Tri Sujaka, Tomy Tri Suprayetno, Djoko Suriyati ., Suriyati Syahrir, Moch. Tajuddin, Muhammad Tri Sujaka, Tomi Wirajaya Kusuma Yuliana Yuliana Zamroni Alpian Muhtarom Zuhrian, Naufal Rifqi Zulfikri, Muhammad