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All Journal Jurnal Riset Informatika JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) bit-Tech Journal of Community Service and Empowerment Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Jurnal Komputer dan Elektro Sains
Budi Mukhamad Mulyo
Universitas Pembangunan Nasional Veteran Jawa Timur
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Mathematics Social Sciences Other

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Community assistance in realizing sustainable garden tourism in Jubung Village, Jember Nanik Hariyana; Budi Mukhamad Mulyo; Kalvin Edo Wahyudi
Journal of Community Service and Empowerment Vol. 6 No. 3 (2025): December
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/jcse.v6i3.42269

Abstract

This community service program aims to support the development of sustainable garden tourism in Jubung Village, Jember, through community assistance and empowerment. Jubung Village has great potential in agricultural-based tourism; however, its management and promotion are still limited. The program was implemented through participatory approaches involving local communities, village officials, and tourism stakeholders. The methods included community training, workshops on sustainable tourism management, branding and digital marketing assistance, as well as the design of eco-friendly tourism activities. The results show that the community gained increased knowledge and skills in managing tourism resources sustainably, developing creative products, and strengthening collaboration among stakeholders. In addition, the program encouraged local awareness of environmental preservation and created new economic opportunities for villagers. This activity demonstrates that continuous community assistance plays a vital role in realizing sustainable garden tourism that not only supports local economic growth but also preserves environmental and socio-cultural values.
Mobile-Based Book Recommendation System Based on Film Preferences Using Content-Based Filtering Nabil Anshari; Retno Mumpuni; Budi Mukhamad Mulyo
bit-Tech Vol. 8 No. 2 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i2.3177

Abstract

The low reading interest among the Indonesian population remains one of the main challenges in improving national literacy quality. One contributing factor is the difficulty in finding reading materials that align with individual interests. Conversely, the increasing public interest in films can be leveraged as a bridge to foster reading habits. This study discusses the development of a mobile-based book recommendation system based on users’ film preferences to facilitate the discovery of books relevant to their favorite films. The proposed method here employs a Content-Based Filtering approach using Term Frequency–Inverse Document Frequency (TF-IDF) and Cosine Similarity to measure the similarity between film synopsis and book descriptions. Data are retrieved in real time through the integration of The Movie Database (TMDB) API and Google Books API. System evaluation was conducted using User Acceptance Testing (UAT) with ISO 9126 as the evaluation framework, focusing on functionality, usability, and reliability aspects. The results show that the application successfully provides relevant book recommendations based on users’ selected films, achieving functionality, usability, and reliability scores of 88%, 84%, and 86%, respectively. Therefore, the system is considered feasible for use and has the potential to serve as a literacy enhancement medium based on film preference.
Rupiah Classification System using Segmented Fractal Texture Analysis and HSV Color Features Ardhon Rakhmadi; Putri Nur Rahayu; Hazna At Thooriqoh; Budi Mukhamad Mulyo
Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Vol. 4 No. 2 (2025): September 2025
Publisher : LKP KARYA PRIMA KURSUS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62712/juktisi.v4i2.560

Abstract

The crime of forgery of rupiah currency can be anticipated by examining the rupiah banknotes based on traits or features contained on the original paper money. Features that are not owned by the rupiah banknote counterfeit is an ultraviolet sign that are owned by the original paper money. Rupiah banknotes feature extraction consists of a combination of color and texture feature extraction. The proposed method is the HSV color histogram for color feature extraction and Segmented Fractal Texture Analysis (SFTA) for texture feature extraction. The combination of HSV and SFTA is expected to improve the performance of rupiah banknotes feature extraction. Moreover this paper will analyze feature redundancy in Two Threshold Decomposition Algorithm in SFTA Algorithm. Experimental results show the proposed method can reach 100% accuracy. Experiment results also show that redundant features can be removed without affecting the accuracy of of the system so that it can reduce the computational cost.
EMPLOYEE PERFORMANCE ASSESSMENT BASED ON MONTHLY PERFORMANCE USING AHP-SAW AT UPPKH PAMEKASAN REGENCY Saiful Abroriy; Firza Prima Aditiawan; Budi Mukhamad Mulyo
Jurnal Riset Informatika Vol. 8 No. 3 (2026): Juni 2026
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v8i3.516

Abstract

Employee performance assessment plays an important role in improving organizational productivity and supporting decision-making processes. However, the evaluation process at UPPKH Pamekasan Regency is still conducted manually, which often leads to subjectivity, inconsistency, and inefficiency. This study aims to develop a Decision Support System (DSS) for employee performance assessment using the combination of Analytical Hierarchy Process (AHP) and Simple Additive Weighting (SAW) methods. The AHP method is used to determine the weight of nine evaluation criteria through pairwise comparison, while the SAW method is applied to rank 20 PKH facilitators based on their performance scores. The system is implemented as a web-based application using the Laravel framework and MySQL database. The results show that the system is able to produce objective and structured rankings, where the highest preference value is obtained by alternative K4 with a score of 0.922. Furthermore, the accuracy of the method is evaluated using the Spearman Rank Correlation test, resulting in a coefficient of 0.97143, which indicates a very strong correlation between the system-generated rankings and the manual rankings from UPPKH. In addition, black box testing confirms that all system functionalities operate correctly. Therefore, the proposed system is effective in reducing subjectivity, improving efficiency, and supporting accurate decision-making in employee performance evaluation.
Implementasi Metode ARAS dalam Sistem Pendukung Keputusan Penentuan Eligible SNBP Rhiziqo Adjie Syahputra; Henni Endah Wahanani; Budi Mukhamad Mulyo
JASIEK (Jurnal Aplikasi Sains, Informasi, Elektronika dan Komputer) Vol. 8 No. 1 (2026): Juni 2026
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jasiek.v8i1.16785

Abstract

This study aims to develop and implement a web-based application designed to assist decision-making in determining eligible students for the National Achievement-Based Selection (SNBP) at SMAN 8 Surabaya. The primary challenge arises from the conventional selection procedures currently in use, which may lead to subjective judgments and inefficiencies when handling multi-criteria data. ARAS method is employed to evaluate both academic and non-academic factors in a systematic manner, resulting in a ranked list of students based on preference values. The application was constructed following the phases of the System Development Life Cycle or SDLC. Analytical results indicate that alternative A2 achieved the highest preference value of 92.50, followed by other alternatives according to the ranking generated by the system. Usability evaluation through the System Usability Scale method (SUS) produced an average score of 88.38, classified within the excellent category, so that the application is user-friendly and appropriate for practical use in the SNBP student selection process
Implementasi CNN Untuk Klasfikasi Emosi Dalam Lagu Berdasarkan Fitur Audio Fredrik Sahalatua Pakpahan; M. Muharrom Al Haromainy; Budi Mukhamad Mulyo
bit-Tech Vol. 8 No. 3 (2026): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i3.3438

Abstract

Music is a powerful art form for conveying and evoking emotions; however, the vast volume of digital music data makes manual emotion categorization difficult. This study aims to implement a Convolutional Neural Network (CNN) to classify emotions in instrumental songs based on audio features. The dataset used is the Database for Emotional Analysis of Music (DEAM), containing 1,802 songs with valence and arousal annotations, which is divided with a 70:15:15 ratio for training, validation, and testing. The feature extraction methods applied include Mel-Frequency Cepstral Coefficients (MFCC) with variations of 13, 24, and 30 coefficients, and Mel-spectrograms with variations of 128, 256, and 512 bins. Data is processed through pre-emphasis and framing stages before being input into a CNN architecture with four convolutional blocks. Evaluation was conducted using 4-quadrant classification scenarios and a simplification into 2 quadrants. The results showed that in the 4-quadrant classification, the best model was achieved using MFCC with 30 coefficients with an accuracy of 66%, but model performance was hindered by extreme minority class imbalance. Conversely, simplifying the emotion space into 2 quadrants (valence or arousal) significantly improved accuracy to 77%. This study concludes that while increasing feature resolution has a minor impact, simplifying emotion dimensions proves more effective in addressing complexity and data imbalance in music emotion classification.
Fusi Metadata dan Masked Mean–Max Pooling dengan DeBERTa-v3 untuk Klasifikasi Tweet Bencana Budi Mukhamad Mulyo; Hazna At Thooriqoh; Ardhon Rakhmadi; Devi Ambarwati Puspitasari; Bayu Permana Sukma
Jurnal Komputer dan Elektro Sains Vol. 4 No. 2 (2026): Komets
Publisher : Sultan Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58291/komets.v4i2.662

Abstract

Klasifikasi tweet terkait bencana merupakan permasalahan penting dalam pemrosesan bahasa alami karena teks media sosial cenderung pendek, tidak baku, dan sering ambigu. Namun, pemanfaatan metadata dan optimasi ambang keputusan secara terpadu pada model transformer masih terbatas. Penelitian ini mengembangkan model klasifikasi biner dengan mengintegrasikan representasi kontekstual DeBERTa-v3, kata kunci, lokasi, dan metadata numerik tweet. Dataset terdiri atas 7.613 data latih dan 3.263 data uji. Teks dibentuk menjadi masukan terstruktur menggunakan penanda kata kunci, lokasi, dan isi tweet. Representasi token dipadatkan melalui masked mean–max pooling, kemudian digabungkan dengan fitur numerik. Model dilatih menggunakan stratified cross-validation, multi-sample dropout, dan optimasi threshold berdasarkan probabilitas out-of-fold. Kebaruan penelitian terletak pada demonstrasi empiris kontribusi metadata fusion, masked mean–max pooling, dan optimasi threshold berbasis out-of-fold dalam melengkapi representasi kontekstual DeBERTa-v3, yang diperkuat melalui evaluasi ablation terkontrol. Pengujian terhadap 15 konfigurasi menunjukkan bahwa DeBERTa-v3 metadata fusion dengan 3-fold out-of-fold ensemble menghasilkan skor F1 tertinggi sebesar 0,84063, melampaui DeBERTa-v3 metadata fusion dengan holdout sebesar 0,83971, DeBERTa-v3 mean–max sebesar 0,83695, DistilBERT sebesar 0,83450, dan GloVe–SVM sebesar 0,81581. Hasil ini menunjukkan bahwa integrasi metadata dan penentuan threshold berbasis out-of-fold efektif meningkatkan kinerja klasifikasi. Penelitian ini berpotensi mendukung penyaringan informasi bencana dari media sosial secara lebih akurat.
Co-Authors Ardhon Rakhmadi Ardhon Rakhmadi Bayu Permana Sukma Devi Ambarwati Puspitasari Firza Prima Aditiawan Fredrik Sahalatua Pakpahan Hazna At Thooriqoh Henni Endah Wahanani Kalvin Edo Wahyudi M. Muharrom Al Haromainy Nabil Anshari Nanik Hariyana Putri Nur Rahayu Retno Mumpuni Rhiziqo Adjie Syahputra Saiful Abroriy