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All Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sisfokom (Sistem Informasi dan Komputer) CYBERNETICS Jurnal PTK dan Pendidikan GERVASI: Jurnal Pengabdian kepada Masyarakat Generation Journal Jurnal Teknika Reforma: Jurnal Pendidikan dan Pembelajaran Jurnal UNITEK Insearch: Information System Research Journal Joutica : Journal of Informatic Unisla Journal of Artificial Intelligence and Engineering Applications (JAIEA)
M. Ghofar Rohman
Universitas Islam Lamongan
Agriculture, Biological Sciences & Forestry Automotive Engineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Other

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PENDAMPINGAN PEMASARAN DIGITAL USAHA RUMAH TANGGA GERABAH DI DESA GENDANGAN MADURAN MELALUI PENGGUNAAN MARKETPLACE Amri, Sholihul; Dhana, Rio Rahma; Rohman, M. Ghofar
GERVASI: Jurnal Pengabdian kepada Masyarakat Vol. 8 No. 1 (2024): GERVASI: Jurnal Pengabdian Kepada Masyarakat
Publisher : LPPM IKIP PGRI Pontianak

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31571/gervasi.v8i1.6527

Abstract

Gedangan Maduran merupakan sebuah desa yang memiliki usaha keluarga yang memproduksi gerabah tradisional. Permasalahan yang dihadapi oleh industri rumah tangga di Desa Gedangan muncul dari kurang maksimalnya penjualan dan perputaran permintaan serta cara pemasaran yang masih konvensional. Tujuan dari dukungan ini adalah: 1) memperluas pengetahuan tentang pendirian dan pengelolaan usaha ritel di pasar; dan 2) meningkatkan keterampilan dalam mengatur dan mengelola operasional penjualan di pasar. Tujuan dari layanan ini terdiri dari dua bagian: 1) pemahaman dan praktik langsung pembuatan akun di pasar bagi peserta perdagangan; 2) pemahaman langsung dan praktis dalam mengelola pemesanan produk pada akun. Pendekatan yang digunakan adalah Asset-Based Community Development (ABCD) yang meliputi lima fase: Definisi, Penemuan, Impian, Desain, dan Takdir. Hasil penelitian ini memungkinkan para pelaku industri lokal di Desa Gedangan memahami pemasaran digital, marketplace dan penjualan produk melalui marketplace. Mereka juga mempunyai peluang untuk menciptakan pasar, mempromosikan produknya di pasar dan meningkatkan penjualan gerabah.
Major Recommendation System for New Students at SMK Muhammadiyah 1 Lamongan with Naive Bayes Algorithm Muzaqi, Wildan Irsyad; Rohman, M. Ghofar; Reknadi, Danang Bagus
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 14 No. 3 (2025): JULY
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v14i3.2390

Abstract

Students' majors in Vocational High Schools (SMK) are very important in determining the direction of their education and career, but the process carried out so far is often subjective and does not consider academic grades and interests objectively. To overcome this, this study develops a website-based major recommendation system at SMK Muhammadiyah 1 Lamongan using the Naive Bayes algorithm that is able to provide accurate major recommendations based on student data. This system is designed using a structured Waterfall Model software development method, starting from needs analysis, design, implementation, to testing. The Naive Bayes algorithm was chosen because of its simplicity and ability to work with relatively small datasets, such as new student data at the school. Of the total 675 student data collected, 60% or 405 data were used as training data to train the Naive Bayes algorithm, while the remaining 40% or 270 data were used as test data to measure the accuracy level of the recommendation system. The test results show that the system achieves an average accuracy of 90.91%, with precision above 0.73 for each major, recall above 0.80 except for the Office Management major which reaches 0.75, and an average F1 score of 81.72%. These findings indicate that the website-based major recommendation system with the Naive Bayes algorithm is effective and can help students determine majors that suit their potential and interests objectively and accurately, thus supporting a more precise and targeted major selection process.
Adaptation of Contrastive Learning and Augmentation for Indonesian Product Review Classification on Unbalanced Data Using Deep Learning and NLP Reknadi, Danang Bagus; Rohman, M. Ghofar; Mustain; Utomo, Aphila Fraga Listyo
Generation Journal Vol 9 No 2 (2025): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v9i2.25783

Abstract

In the digital era, product reviews are an important source of information for consumers and businesses because they influence purchasing decisions and marketing strategies. However, the distribution of sentiment in product reviews is often unbalanced, with positive reviews dominating and negative reviews being limited. This condition poses a challenge in developing text classification models, especially for Indonesian which has a complex morphological structure and very rich vocabulary variations. This study adapts the Contrastive Learning method for the classification of unbalanced Indonesian language product reviews and tests the effectiveness of text augmentation techniques in improving representation, especially for minority classes with limited data. Data were obtained through web scraping from Indonesian e-commerce platforms, totaling around 10,000 reviews with a composition of 52% positive, 30% negative, and 18% neutral. The data was processed and expanded using augmentation techniques to significantly increase the variety and amount of training data. The LSTM model trained on the original data and the augmented data, showing an increase in validation accuracy from around 73% to almost 100% in the 30th epoch, with a final accuracy reaching 92% and an F1-Score of 90%. These results confirm that the incorporation of data augmentation is crucial to address imbalance, thereby improving the robustness and reliability of the model in product review sentiment classification
Implementasi Sistem Pakar Diagnosa Kerusakan Televisi Berbasis Web Menggunakan Metode Certainty Factor Alawi, Thoriq Achmad; Rohman, M Ghofar; Munif
JURNAL UNITEK Vol. 18 No. 2 (2025): Juli-Desember 2025
Publisher : Sekolah Tinggi Teknologi Dumai

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52072/unitek.v18i2.1601

Abstract

Televisi merupakan salah satu perangkat elektronik yang umum digunakan masyarakat, namun rentan mengalami berbagai jenis kerusakan. Proses identifikasi kerusakan biasanya memerlukan keahlian teknis dan pengalaman, sehingga menjadi kendala bagi teknisi pemula maupun pengguna awam. Penelitian ini bertujuan untuk mengembangkan sistem pakar berbasis web yang dapat mendiagnosa kerusakan televisi menggunakan metode Certainty Factor (CF). Metode ini dipilih karena mampu mengakomodasi ketidakpastian data dan memberikan tingkat keyakinan dalam bentuk persentase terhadap hasil diagnosa. Data gejala, jenis kerusakan, serta bobot Measure of Belief (MB) dan Measure of Disbelief (MD) diperoleh melalui wawancara dengan pakar servis televisi berpengalaman. Pengujian sistem dilakukan dengan metode blackbox dan validasi pakar. Hasil penelitian menunjukkan bahwa sistem mampu memberikan diagnosa kerusakan televisi dengan tingkat akurasi sebesar 90%. Sistem ini dinilai efektif membantu proses identifikasi kerusakan dengan menampilkan tingkat kepastian diagnosa dan saran perbaikan yang sesuai. Penelitian ini diharapkan dapat menjadi referensi pengembangan sistem pakar untuk perangkat elektronik lainnya.
Implementation of the SAW Method for Mobile Phone Selection Recommendations at Holida Seluler Store Mu'afi, Achmad Faiq; Rohman, M Ghofar; Zamroni, Moh Rosidi
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.26505

Abstract

Public demand for mobile phones continues to increase as mobile phones evolve as tools for communication, work, entertainment, and access to digital information. With so many products with varying specifications to choose from, consumers often find it difficult to determine which mobile phone suits their needs. Holida Seluler, a store that sells various types of mobile phones, still uses a manual approach in providing recommendations to customers, which can potentially result in inaccurate decisions. This study aims to develop a website using the Simple Additive Weighting (SAW) method to assist customers in determining the best mobile phone, as well as to design a system capable of presenting objective calculation results based on predetermined criteria weights that can be directly applied in the recommendation process. The data used consists of 50 mobile phone products available in stores, with seven main criteria, namely: price, RAM, internal memory, camera, battery capacity, screen, and refresh rate. This system was built using the PHP programming language and MySQL database. The implementation results show that the system can objectively rank mobile phones based on user preferences, with the A45 alternative as the best choice, obtaining the highest score of 0.9100. This system is capable of providing fast, accurate, and data-driven recommendations, thereby increasing service effectiveness and enhancing the customer experience in choosing the right product
Decision Support System For Determining The Major Of New Students At SMKS Sunan Drajat Sugio Using The Method SAW (Simple Additive Weighting) Rohma, Riska Dwi Elida Yahyatul; Rohman, M. Ghofar; Zamroni, M. Rosidi
Generation Journal Vol 10 No 1 (2026): Generation Journal
Publisher : Universitas Nusantara PGRI Kediri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29407/gj.v10i1.26553

Abstract

Determining majors for new students at vocational schools is an important process that can affect student achievement and future prospects. However, this process is often still carried out manually and is not very objective, which can potentially lead to a mismatch between the chosen major and the potential and interests of the students. Therefore, a system is needed that can help schools determine majors more accurately, efficiently, and based on data. This study aims to design a Decision Support System (DSS) for determining the majors of new students at SMKS Sunan Drajat Sugio using the Simple Additive Weighting (SAW) method. The SAW method was chosen because of the ease of calculation and its ability to process multi-criteria data to produce systematic decisions. The criteria used in this system include report card averages, basic competency test results, and student interests. This system is web-based with a user-friendly interface. Testing was conducted using 65 new student data for the 2024/2025 academic year by comparing the system's calculation results with manual calculations using the SAW method that had been validated by the school. The test results showed a 100.0% match between the system results and manual calculations, indicating that the system is capable of implementing the SAW method accurately and consistently. Thus, the developed system can be used as a tool to assist
Pengenalan teknologi pembelajaran translanguaging berbasis web untuk meningkatkan literasi bahasa Arab-Indonesia di SMA Muhammadiyah 1 Babat Danang Bagus Reknadi; Siti Mujilahwati; Sugeng Dwi Hartantyo; M. Ghofar Rohman; Sholihul Amri; Uzlifatul Masruroh Isnawati
SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Vol 9, No 6 (2025): November
Publisher : Universitas Muhammadiyah Mataram

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31764/jpmb.v9i6.35722

Abstract

AbstrakKegiatan pengabdian kepada masyarakat ini bertujuan untuk mengenalkan dan mengimplementasikan teknologi translanguaging berbasis web sebagai sarana pendukung peningkatan literasi pembelajaran Bahasa Arab-Indonesia bagi siswa. Media pembelajaran yang tersedia sebelumnya masih terbatas dan belum mampu mengintegrasikan bahasa Arab dan bahasa Indonesia dalam satu platform, sehingga siswa mengalami kesulitan memahami materi secara lebih kontekstual. Kegiatan ini dilaksanakan di SMA Muhammadiyah 1 Babat, dengan melibatkan guru Bahasa Arab dan siswa sebagai peserta uji coba. Proses pengabdian dilakukan melalui analisis kebutuhan, perancangan aplikasi, pengembangan berbasis web, dan uji coba lapangan. Data diperoleh melalui penyebaran angket kepuasan serta observasi langsung terhadap aktivitas belajar mengajar. Tingkat kepuasan guru dan siswa diukur menggunakan kuesioner dengan skala penilaian terhadap kemudahan penggunaan, tampilan, dan manfaat aplikasi dalam proses pembelajaran. Aplikasi translanguaging ini dilengkapi fitur terjemahan kontekstual dua arah, kamus interaktif, dan latihan pemahaman teks yang membantu siswa meningkatkan literasi bahasa serta memudahkan guru dalam penyampaian materi.. Hasil uji menunjukkan tingkat kepuasan guru sebesar 87% dan siswa sebesar 90%, yang menandakan aplikasi ini diterima dengan baik. Secara keseluruhan, kegiatan ini membuktikan bahwa teknologi translanguaging berbasis web efektif mendukung literasi pembelajaran Bahasa Arab-Indonesia, dengan ruang lingkup yang masih dapat diperluas pada jenjang pendidikan lain agar manfaatnya semakin luas. Kata kunci: translanguaging; literasi; berbasis web; bahasa Arab-Indonesia; teknologi pembelajaran. AbstractThis community service activity aims to introduce and implement web-based translanguaging technology as a means of supporting students' Arabic-Indonesian language learning literacy. Previously available learning media were limited and unable to integrate Arabic and Indonesian into a single platform, resulting in students having difficulty understanding the material more contextually. This activity was carried out at SMA Muhammadiyah 1 Babat, involving Arabic language teachers and students as trial participants. The community service process was carried out through needs analysis, application design, web-based development, and field trials. Data were obtained through the distribution of satisfaction questionnaires and direct observation of teaching and learning activities. Teacher and student satisfaction levels were measured using questionnaires with a rating scale for ease of use, appearance, and the application's usefulness in the learning process. This translanguaging application is equipped with a two-way contextual translation feature, an interactive dictionary, and text comprehension exercises that help students improve language literacy and facilitate teachers in delivering material. The test results showed a teacher satisfaction level of 87% and a student satisfaction level of 90%, indicating that the application was well received. Overall, this activity proves that web-based translanguaging technology effectively supports Arabic-Indonesian language learning literacy, with a scope that can still be covered at other levels of education so that its benefits are even broader. Keywords: translanguaging; literacy; web-based; Arabic-Indonesian; learning technology.
MACHINE LEARNING TO IDENTIFY ELIGIBILITY OF STUDENTS RECEIVING SINGLE TUITION RELIEF Rohman, M. Ghofar; Abdullah, Zubaile; Kasim, Shahreen; Albab, M Ulul
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 3 (2026): JITK Issue February 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i3.7294

Abstract

The cost of higher education in Indonesia varies greatly and often becomes a financial burden for students. Socio-economic factors such as parental income, occupation, number of dependents, vehicle ownership, and place of residence influence the determination of single tuition as regulated by the Ministry of Education Regulation No. 55 of 2013. This study aims to classify freshmen eligibility for single tuition relief using five machine learning models: RF, LR, KNN, SVM, and NB. The dataset contains 2000 rows of data with six socio-economic attributes divided into two classes: eligible and ineligible. The data were split into 80% training and 20% testing, and model performance was evaluated using accuracy, precision, recall, F1-score, and ROC-AUC. Results show that without SMOTE, all models suffer from severe majority-class bias, yielding critically low recall for the minority class  SVM = 0.014; NB = 0.004. SMOTE significantly improves minority-class detection, with RF and SVM achieving the highest performance F1-scores of 0.820 and 0.801, and ROC-AUC of 0.966 and 0.990, respectively. SHAP analysis identifies Number of Dependents of Parents as the most influential predictor across all models, highlighting its central role in financial need assessment. These findings demonstrate that combining SMOTE with ensemble or margin-based models enhances classifiying  fairness and sensitivity in educational support systems. The future work recommend expanding features to include behavioral, academic, and regional indicators, using multi-institutional data, and exploring deep learning or advanced resampling methods to enhance generalizability and robustness
Implementation of the Content-Based Filtering Method in Menu Recommendations at Pandawa Pondok Kopi Saputra, Muhammad Hanes Eka; Rohman, M.Ghofar; Zamroni, M.Rosidi
Journal of Artificial Intelligence and Engineering Applications (JAIEA) Vol. 5 No. 2 (2026): February 2026
Publisher : Yayasan Kita Menulis

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59934/jaiea.v5i2.1838

Abstract

The rapid growth of the coffee shop industry and the wide variety of menu offerings at Pandawa Pondok Kopi demand a system capable of delivering accurate and personalized menu recommendations. This study aimed to develop a web based menu recommendation application using Content Based Filtering (CBF), leveraging TF-IDF for document vectorization and Cosine Similarity to measure product description similarity.The system was implemented with PHP and MySQL, featuring a responsive interface across three main modules: the homepage (displaying the menu list), the menu detail page (providing full information and similar recommendations), and the admin dashboard (for menu data management). Menu descriptions were preprocessed (tokenization, stop word removal, and stemming) before computing TF-IDF weights. Given a user’s selected menu item, the system calculated Cosine Similarity between its description vector and those of all other menu items, then presents the top three matches. Functionality was verified via Black Box Testing to ensure that admin login, menu addition/editing, recommendation displays, and interface navigation conform to specifications. Test results showed an average Cosine Similarity score ranging from 0.62 to 0.78, indicating satisfactory accuracy in matching user preferences. The system also achieved an average response time of under one second under standard load, meeting efficiency criteria.In conclusion, the Content Based Filtering implementation successfully enhances the relevance of menu recommendations and user experience, thereby supporting increased customer satisfaction and operational effectiveness at Pandawa Pondok Kopi.
Co-Authors Abdullah, Zubaile Agung Ferry Anggara Alawi, Thoriq Achmad Amri, Sholihul Arif Irawan Bachri, Affan Bettaliyah, Azza Abidatin Danang Bagus Reknadi Erin masfufah Farida, Mega Fitri Rohmawati Ihza, Muhammad Yusril Laksono, Arief Budi M Hasan Wahyudi M. Rosidi Zamroni M. Ulul Albab Masruroh Masruroh Masruroh Masruroh Miftahus Sholihin Mu'afi, Achmad Faiq Munif MUNIF ROCHMAWANTO Mustain Muzaqi, Wildan Irsyad Nurul Fuad Pratiwi, Putri Septiani Indah Prisma Nanda Purnomo Hadi Susilo, Purnomo Hadi Purnomo Hadi Susiolo Putra Samudra Putri, Jenjang Oktaviasanata Ananda Rahma Dhana, Rio Rohma, Riska Dwi Elida Yahyatul Rohman, Auliya’ur Saputra, Muhammad Hanes Eka Shahreen Kasim, Shahreen Siti Mujilahwati Sugeng Dwi Hartantyo Sundiya Eryfa Diana Suryanti, Cindy Syarif Hidaytullah, Athia Utomo, Aphila Fraga Listyo Wulandari, Dewi Sri Yahya, Kurnia Zamroni, Moh Rosidi Zumrotus Shalekhah