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All Journal JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab 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) Jurnal Ilmiah Sistem Informasi
M. Ghofar Rohman
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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
Hybrid Logistic Regression Random Forest on Predicting Student Performance Rohman, Muhammad Ghofar; Abdullah, Zubaile; Kasim, Shahreen; Rasyidah, -
JOIV : International Journal on Informatics Visualization Vol 9, No 2 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.2.3972

Abstract

The research aims to investigate the effects of unbalanced data on machine learning, overcome imbalanced data using SMOTE oversampling, and improve machine learning performance using hyperparameter tuning. This study proposed a model that combines logistic regression and random forests as a hybrid logistic regression, random forest, and random search SV that uses SMOTE oversampling and hyperparameter tuning. The result of this study showed that the prediction model using the hybrid logistic regression, random forest, and random search SV that we proposed produces more effective performance than using logistic regression and random forest, with accuracy, precision, recall, and F1-score of 0.9574, 0.9665, 0.9576. This can contribute to a practical model to address imbalanced data classification based on data-level solutions for student performance prediction.
Sistem Prediksi Penjualan Produk pada STARMART Menggunakan Metode Linear Regression dan Weighted Moving Average Adiyatma, Dhias Arsyah; Rohman, Muhammad Ghofar; Munif, Munif
Jurnal Ilmiah Sistem Informasi Vol. 4 No. 3 (2025): November: Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/g260jt34

Abstract

Penelitian ini bertujuan untuk membuat sistem prediksi penjualan berbasis web pada Starmart dengan menerapkan dua metode, yaitu Linear Regression dan Weighted Moving Average. Penelitian dilakukan dengan beberapa tahap, yaitu pengumpulan data penjualan, pengolahan data, penerapan kedua metode prediksi, serta pengujian akurasi menggunakan Mean Squared Error (MSE) dan Mean Absolute Percentage Error (MAPE).Penulis melakukan pengumpulan data secara langsung yang diambil dari Starmart dengan cakupan data penjualan yang berjumlah 12 kategori produk,Kemudian data diproses untuk membangun aplikasi prediksi penjualan yang bertujuan untuk memprediksi penjualan produk pada Starmart untuk periode selanjutnya.Hasil penelitian menunjukkan bahwa implementasi metode Linear Regression memberikan hasil prediksi yang lebih stabil dan sesuai dengan pola data historis, dengan nilai MSE sebesar 2184.18 dan MAPE sebesar 10.88%. Sementara itu, metode Weighted moving average menghasilkan prediksi yang cenderung fluktuatif dengan nilai MSE sebesar 4715.66 dan MAPE sebesar 17.35%. Berdasarkan perbandingan kedua metode, dapat disimpulkan bahwa Linear regression lebih akurat dibandingkan Weighted Moving Average dalam memprediksi penjualan produk di Starmart.
Sistem Pakar Diagnosa Penyakit pada Sapi Menggunakan Naive Bayes Raudha, Yoevita; Rohman, Muhammad Ghofar; Munif
Jurnal Ilmiah Sistem Informasi Vol. 4 No. 2 (2025): Mei : Jurnal Ilmiah Sistem Informasi
Publisher : LPPM Universitas Sains dan Teknologi Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pmxpdm56

Abstract

Sapi merupakan salah satu hewan ternak yang memiliki nilai ekonomi tinggi, namun rentan terhadap berbagai penyakit, khususnya penyakit pernapasan. Untuk mendukung proses diagnosis penyakit secara cepat dan akurat, diperlukan sistem pakar berbasis teknologi. Penelitian ini bertujuan untuk mengembangkan sistem pakar yang mampu mendiagnosis penyakit pernapasan pada sapi dengan menggunakan metode Naïve Bayes. Metode ini dipilih karena mampu mengolah data dengan pendekatan probabilistik yang efisien serta menghasilkan prediksi yang akurat meskipun dengan data terbatas. Sistem dirancang dalam bentuk aplikasi berbasis web menggunakan bahasa pemrograman PHP dan basis data MySQL. Data gejala dan penyakit diperoleh melalui wawancara dengan dokter hewan, mencakup 16 gejala dan 9 jenis penyakit pernapasan yang umum terjadi, kemudian digunakan sebagai data latih dalam proses klasifikasi. Sistem menerapkan perhitungan probabilitas melalui tahapan prior, likelihood, dan posterior untuk menghasilkan diagnosis yang paling mungkin. Pengujian dilakukan dengan membandingkan hasil diagnosa sistem dengan diagnosa pakar, dan evaluasi menunjukkan bahwa sistem mencapai tingkat akurasi sebesar 90%, yang membuktikan keandalannya dalam penerapan praktis. Selain memberikan hasil diagnosis berdasarkan gejala yang dipilih pengguna, sistem ini juga menampilkan deskripsi penyakit dan saran penanganan. Dengan adanya fitur tersebut, sistem pakar ini diharapkan dapat membantu peternak dalam mendeteksi penyakit pernapasan pada sapi lebih dini, mempercepat proses penanganan, mengurangi kerugian ekonomi, serta menjadi alat teknologi yang mudah diakses untuk mendukung praktik kedokteran hewan di wilayah pedesaan.
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.
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 M. Ghofar Rohman; Zubaile Abdullah; Shahreen Kasim; M Ulul Albab
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
KLASIFIKASI KUALITAS UDARA DENGAN METODE NAIVE BAYES BERBASIS WEB: AIR QUALITY CLASSIFICATION USING WEB-BASED NAIVE BAYES METHOD Sugeng Dwi Budi Priantoro; M Ghofar Rohman; Moh Rosidi Zamroni
Rabit : Jurnal Teknologi dan Sistem Informasi Univrab Vol 10 No 2 (2025): Juli
Publisher : LPPM Universitas Abdurrab

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36341/rabit.v10i2.6447

Abstract

Air quality is a critical indicator for public health and the environment. This study presents the first web‑based implementation for classifying the Air Pollutant Standard Index (ISPU) of Jakarta using the 2024 dataset from Satu Data Indonesia. The Gaussian Naive Bayes method was chosen for its efficiency and ability to handle continuous numerical data. Preprocessing steps included mean imputation, removal of “no data” and “very unhealthy” categories, and a random state 80:20 train‑test split. Evaluation results show 90.57% accuracy surpassing the KNN baseline of 86% with precision and recall F1‑scores for the “Unhealthy” category at 84.09% and 92.50%, respectively. A Flask‑based web application air quality prediction. These findings confirm the superiority of Gaussian Naive Bayes over KNN in handling data imbalance, while providing an accurate, accessible environmental monitoring tool. Contributions of this research include (1) the first deployment of ISPU Jakarta 2024 in a web system, and (2) a measured performance comparison between GNB and KNN.
Co-Authors Abdullah, Zubaile Achmad Faiq Mu'afi Adiyatma, Dhias Arsyah 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 M.Rosidi Zamroni Masruroh Masruroh Masruroh Masruroh Miftahus Sholihin Moh Rosidi Zamroni Muhammad Hanes Eka Saputra Munif MUNIF ROCHMAWANTO Munif, Munif 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 Rasyidah, - Raudha, Yoevita Riska Dwi Elida Yahyatul Rohma Rohman, Auliya’ur Shahreen Kasim Shahreen Kasim, Shahreen Siti Mujilahwati Sugeng Dwi Budi Priantoro Sugeng Dwi Hartantyo Sundiya Eryfa Diana Suryanti, Cindy Syarif Hidaytullah, Athia Utomo, Aphila Fraga Listyo Wulandari, Dewi Sri Yahya, Kurnia Zubaile Abdullah Zumrotus Shalekhah