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All Journal Plano Madani : Jurnal Perencanaan Wilayah dan Kota Indonesian Journal of Artificial Intelligence and Data Mining JOURNAL OF APPLIED INFORMATICS AND COMPUTING JISICOM (Journal of Information System, Infomatics and Computing) JSAI (Journal Scientific and Applied Informatics) Jurnal Informatika dan Rekayasa Elektronik Cyber Security dan Forensik Digital (CSFD) Jurnal Teknologi Informatika dan Komputer Walisongo Journal of Information Technology Journal La Multiapp JURNAL ILMIAH GLOBAL EDUCATION Warta LPM Software Development Digital Business Intelligence and Computer Engineering Jurnal Pengabdian KOLABORATIF
Mustofa, Hery
Universitas Islam Negeri Walisongo Semarang
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Mathematics Social Sciences Transportation Other

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Perancangan dan Implementasi Massive Open Online Course (MOOC) untuk Pembelajaran Agama Islam Hery Mustofa; Khoirul Adib
Jurnal Teknologi Informatika dan Komputer Vol 8, No 2 (2022): Jurnal Teknologi Informatika dan Komputer
Publisher : Universitas Mohammad Husni Thamrin

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37012/jtik.v8i2.1179

Abstract

Belajar agama merupakan kebutuhan setiap insan manusia. Setiap orang memerlukan belajar agama yang mudah diakses dan digunakan secara fleksibel kapan pun dan di mana pun. Setiap orang mempunyai aktivitas yang beragam,  kadang tidak sempat untuk memperhatikan pendidikan agama. Penelitian ini akan penelitian akan dilakukan pembuatan web berbasis massive open online course (MOOC) untuk pembelajaran agama Islam  sebagai wujud untuk melakukan akselerasi belajar agama berbasis platform digital. Pembuatan sistem tersebut menggunakan metode pengembangan sistem waterfall. Dari hasil pengujian UAT kepada 36 responden didapatkan hasil 81% yang artinya sistem sudah berjalan dengan baik.
Clustering of Tuberculosis and Normal Lungs Based on Image Segmentation Results of Chan-Vese and Canny with K-Means Fayza Nayla Riyana Putri; Nur Cahyo Hendro Wibowo; Hery Mustofa
Indonesian Journal of Artificial Intelligence and Data Mining Vol 6, No 1 (2023): Maret 2023
Publisher : Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/ijaidm.v6i1.21835

Abstract

The lungs are a vital organ in the human body. If there is interference with lung function, the health of the human body as a whole can be affected. Examination by medical workers needs to be done when there is interference with lung function. This examination can usually be done in various ways, one of which is through a chest X-ray radiographic examination procedure. The application of Artificial Intelligence is growing rapidly in the medical field, especially in diagnostics and treatment management. Artificial intelligence in the medical world can also be applied in processing image data in radiology to analyze X-ray results as supporting diagnostic information. Operators Chan-Vese and Canny are two edge detection operators in digital image processing in an effort to obtain the necessary information based on the shape and size of the object. This study was conducted for clustering of normal and tuberculosis lung conditions based on the results of chest X-ray image segmentation from Chan-Vese and Canny using K-Means Clustering. The results of clustering using K-Means obtained an accuracy value of 77.1%, a precision value of 88%, and a specificity value of 97.2%
Penguatan Metode Computational Thinking untuk Guru Madrasah dalam Rangka Meningkatkan Minat Belajar Siswa Pasca Pandemi Covid-19 Kustomo; Lulu Choirun Nisa; Hery Mustofa
Warta LPM WARTA LPM, Vol. 26, No. 1, Januari 2023
Publisher : Universitas Muhammadiyah Surakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1293.353 KB) | DOI: 10.23917/warta.v26i1.799

Abstract

Madrasah teachers are currently considered to have lower cognitive competence when compared to formal school teachers in general, although this statement is not entirely correct. Researchers have conducted computational thinking training in several madrasa of Central Java Provinces, like MIN 1 Kendal, MTs N 1 Jepara, and MAN 1 Grobogan. Computational Thinking (CT) involves problem solving and system design by breaking it down into several stages that are effective, efficient, and comprehensive, including decomposition, pattern recognition, abstraction, and algorithms which are some of the basic concepts of computer science. The purpose of this study is the implementation of CT by madrasa’ teachers on each lesson to students in order to increase student learning interest. The research method used is blended learning which is a combination of an online course (introduction to Bebras Indonesia and CT) and an onsite course (training on CT and implementation of CT to students). The results showed that there was an increase in the average score of the trainees between the pre-test and post-test of the teachers at MIN 1 Kendal, MTs N 1 Jepara, and MAN 1 Grobogan i.e. 70.23%, 70.01% and 80. 64%, respectively. Furthermore, student testimonials after the implementation of CT in subjects taught by the majority of teachers at 66.79% filled in very interesting so that CT learning was very effective in increasing student learning interest in madrasah.
Pendampingan Pengelolaan Jurnal Online di Universitas Selamat Sri (UNISS) Kendal Nur Khasanah; Hery Mustofa; Adzhal Arwani Mahfudh; Syaiful Bakhri
Jurnal Pengabdian KOLABORATIF Vol 1, No 1 (2023): January
Publisher : Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/jpk.v1i1.6053

Abstract

The existence of scientific journals is fundamental as a place for the publication of scientific articles for academics to show the results of their research work. Along with the development of technology and information, it demands the management of online-based journals for the convenience and transparency of the articles in them. Management of journals with an online system is still not fully understood by academics in several institutions. As one of the universities located not far from UIN Walisongo Semarang, Selamat Sri University (UNISS) Kendal has sufficient resources and according to needs to be a place for community service activities to be carried out with the theme of assisting in the management of online journals based on the Open Journal System (OJS). The purpose of this assistance is to introduce online scientific journal management using the OJS system at UNISS. The method used is lectures and hands-on practice regarding online journal management. The results of this community service activity are knowing information about the availability of infrastructure, articles, Human Resources (HR), and regulations that support the start of managing online journals using OJS. In addition, understanding and awareness of the need to have online journals for the benefit of scientific publications for researchers or academics are also obtained which is the requirement as stated in the Tri Dharma of Higher Education.
KLASIFIKASI CITRA TUMOR OTAK MENGGUNAKAN TEKNIK TRANSFER LEARNING PADA ARSITEKTUR RESNET-50 Salsabila, Yusrina Ilmi; Mustofa, Hery; Ulinuha, Masy Ari
Journal of Information System, Informatics and Computing Vol 9 No 1 (2025): JISICOM (June 2025)
Publisher : Sekolah Tinggi Manajemen Informatika dan Komputer Jayakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52362/jisicom.v9i1.1925

Abstract

Penelitian ini bertujuan untuk mengimplementasikan dan mengevaluasi performa model deep learning Convolutional Neural Network (CNN) berbasis arsitektur ResNet-50 dalam klasifikasi citra Magnetic Resonance Imaging (MRI) tiga jenis tumor otak: glioma, meningioma, dan pituitary. Pendekatan transfer learning digunakan dengan dua skenario fine-tuning, yaitu pembekuan 30 layer pertama dan pembekuan 15 layer pertama. Dataset terdiri dari 3.064 citra MRI yang dibagi ke dalam data latih dan uji dengan rasio 80:20. Citra diproses melalui tahapan resizing, normalisasi, dan augmentasi untuk meningkatkan variasi data. Model dievaluasi menggunakan metrik akurasi, precision, recall, F1-score, confusion matrix, dan ROC-AUC. Hasil menunjukkan bahwa model dengan freeze 15 layer memberikan akurasi lebih tinggi sebesar 91,86% dibandingkan freeze 30 layer sebesar 90,88%. Namun, model dengan freeze 30 layer menunjukkan kestabilan dan generalisasi yang lebih baik terhadap data uji, terutama dalam mendeteksi meningioma. Temuan ini menunjukkan bahwa ResNet-50 efektif dalam klasifikasi tumor otak berbasis MRI, dan fine-tuning yang tepat berpengaruh terhadap performa akhir model.This study aims to implement and evaluate the performance of a deep learning Convolutional Neural Network (CNN) model based on the ResNet-50 architecture for classifying Magnetic Resonance Imaging (MRI) brain tumor images into three types: glioma, meningioma, and pituitary. A transfer learning approach was applied using two fine-tuning scenarios: freezing the first 30 layers and freezing the first 15 layers. The dataset consisted of 3,064 MRI images, split into training and testing data at an 80:20 ratio. Images were processed through resizing, normalization, and augmentation to enhance data diversity. The model was evaluated using accuracy, precision, recall, F1-score, confusion matrix, and ROC-AUC metrics. Results showed that the 15-layer freeze model achieved a higher accuracy of 91.86% compared to the 30-layer freeze model at 90.88%. However, the 30-layer freeze model demonstrated better stability and generalization on the test data, particularly in detecting meningioma. These findings indicate that ResNet-50 is effective for MRI-based brain tumor classification, and proper fine-tuning significantly influences model performance.
TINGKAT KEMATANGAN TATA KELOLA TEKNOLOGI INFORMASI MENGGUNAKAN METODE TESCA Mustofa, Hery; Bakhri, Syaiful
JSAI (Journal Scientific and Applied Informatics) Vol 3 No 3 (2020): November
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v3i3.1159

Abstract

Evaluasi tata kelola TIK menjadi bagian penting dalam penerapan TIK dalam sebuah perguruan tinggi. Tata kelola teknologi informasi yang baik akan membantu mempercepat terwujudnya visi dan misi sebuah perguruan tinggi. Pusat Teknologi Informasi dan Pangkalan Data (PTIPD) UIN Walisongo merupakan unit teknis yang  mempunyai tugas mengelola dan mengembangkan sistem informasi di lingkungan Institusi. Mengingat pentingnya PTIPD maka perlu dilakukan evaluasi tingkat kematangan teknologi informasi dengan menggunakan metode TeSCA. Analisis dilakukan dengan teknik observasi, wawancara, penelaah dokumen dan konfirmasi untuk mendukung analisis terhadap PTIPD. Hasil analisis menunjukkan bahwa tingkat kematangan TIK UIN Walisongo pada tingkat Level Madya dengan scores total 55.43.
HANA: An AI Chatbot for Islamic Jurisprudence on Menstruation using SBERT and TF-IDF Masuzzahra, Tsaura Rafah; Khothibul Umam; Hery Mustofa; Maya Rini Handayani
Journal of Applied Informatics and Computing Vol. 9 No. 3 (2025): June 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i3.9449

Abstract

The advancement of Artificial Intelligence (AI), particularly in Natural Language Processing (NLP), has opened new opportunities for religious technological innovation, especially in addressing practical Islamic jurisprudence issues such as menstruation (fiqh haid). This research proposes and implements HANA, an AI chatbot developed for Telegram, utilizing a hybrid approach combining Term Frequency-Inverse Document Frequency (TF-IDF) and Sentence-BERT (SBERT) models. A curated dataset of over 1000 question-answer pairs from classical and contemporary Islamic literature was used, primarily based on the Shafi'i school of thought. The chatbot matches user queries through a two-stage retrieval: initial keyword matching via TF-IDF and deeper semantic matching via SBERT embeddings. Evaluations were conducted by comparing TF-IDF, SBERT, and hybrid approaches using cosine similarity, precision, recall, and F1-score metrics, focused on top-1 retrieval accuracy. HANA achieved an average cosine similarity score of 0.6581 and a semantic relevance rating of 87% based on expert validation, while User Acceptance Testing (UAT) involving 15 respondents indicated 86.7% satisfaction. Although the system is deployed as a proof-of-concept on Google Colab without persistent hosting, it demonstrates the viability of lightweight AI chatbots for Shariah consultation services. Future improvements include multi-turn conversation handling and integration with large language models for better context understanding. This research contributes to expanding NLP applications within techno-dakwah initiatives, providing a scalable approach to enhance women's access to Islamic jurisprudence knowledge.
Komparasi Algoritma Klasifikasi untuk Analisis Sentimen Kinerja Dosen Mustofa, Hery
Software Development, Digital Business Intelligence, and Computer Engineering Vol. 4 No. 01 (2025): SESSION (SEPTEMBER)
Publisher : Politeknik Negeri Banyuwangi Jl. Raya Jember km. 13 Labanasem, Kabat, Banyuwangi, Jawa Timur (68461) Telp. (0333) 636780

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57203/session.v4i01.2025.08-15

Abstract

Dalam era digital, data teks yang berasal dari ulasan, komentar, dan feedback online mahasiswa dapat menjadi sumber informasi berharga untuk memahami persepsi positif dan negatif terhadap kinerja dosen. Penelitian ini bertujuan untuk melakukan komparasi algoritma klasifikasi untuk analisis sentimen opini atau persepsi mahasiswa terhadap kinerja dosen di pendidikan tinggi. Dalam penelitian ini, dilakukan perbandingan tiga algoritma klasifikasi yaitu Naïve Bayes, Support Vector Machine, dan Random Forest. Datasets yang digunakan berupa ulasan mahasiswa terhadap kinerja dosen dari berbagai mata kuliah dan dosen. Datasets terdiri dari 1254 data komentar kritik saran mahasiswa. Data tersebut terdiri dari 839 komentar positif dan 415 komentar negatif. Selanjutnya dilakukan klasifikasi menggunakan algoritma Naïve Bayes, Support Vector Machine, serta Random Forest. Dari hasil klasifikasi diketahui bahwa algoritma Support Vector Machine memberikan hasil yang paling baik kemudian disusul dengan algoritma Random Forest dan yang terakhir algoritma Naïve Bayes. Diketahui Algoritma Support Vector Machine berhasil mendapatkan nilai accuracy sebesar 82,24%, dengan nilai precission sebesar 84,66%, nilai recall sebesar 65,99% dan nilai F1 score sebesar 79,81%.
Application of SVM and Naive Bayes with PSO for the Classification of Saloka Amusement Park Reviews Putri, Indira Alifia; Umam, Khothibul; Handayani, Maya Rini; Mustofa, Hery
Journal La Multiapp Vol. 6 No. 6 (2025): Journal La Multiapp
Publisher : Newinera Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37899/journallamultiapp.v6i6.2505

Abstract

Visitor opinions on tourist destinations can be evaluated through sentiment analysis based on textual reviews. This study aimed to compare the performance of Support Vector Machine (SVM) and Naive Bayes (NB) algorithms in classifying visitor sentiments toward reviews of Saloka Theme Park, while also assessing the impact of parameter optimization using Particle Swarm Optimization (PSO). A total of 740 reviews were collected from the Traveloka platform and underwent text preprocessing. The optimization process targeted key parameters of each algorithm to improve the F1-score. Experimental results showed that the unoptimized SVM achieved an accuracy of 89 percent, while NB reached 86 percent. After applying PSO, SVM's accuracy dropped to 84 percent, whereas NB improved to 85 percent with more balanced classification across sentiment classes. These results recommend the integration of Naive Bayes with Particle Swarm Optimization as a potential approach for sentiment classification of tourism reviews, particularly in the case study of Saloka Theme Park.
Opinion Classification on IMDb Reviews Using Naïve Bayes Algorithm Putri, Amiliya; Umam, Khothibul; Mustofa, Hery
Journal of Applied Informatics and Computing Vol. 9 No. 6 (2025): December 2025
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v9i6.9831

Abstract

This study aims to classify user opinions on IMDb movie reviews using the Multinomial Naïve Bayes algorithm. The dataset consists of 50,000 reviews, evenly distributed between 25,000 positive and 25,000 negative reviews. The preprocessing stage includes cleaning, case folding, stopword removal, tokenization, and lemmatization using the NLTK library. Text features are represented through the TF-IDF method to capture the significance of each word in the documents. The Multinomial Naïve Bayes model was trained using the hold-out validation technique with an 80:20 split for training and testing data. Hyperparameter tuning of α (Laplace smoothing) was conducted to enhance model stability and accuracy. The model’s performance was evaluated using accuracy, precision, recall, and F1-score metrics, supported by a confusion matrix visualization. The results show that the model achieved an accuracy of 87%, with precision of 87.9%, recall of 85.4%, and an F1-score of 86.6%. In comparison, Logistic Regression as a baseline algorithm achieved an accuracy of 91%. Nevertheless, the Naïve Bayes algorithm remains competitive and computationally efficient for large-scale text data, making it highly relevant for sentiment analysis of movie reviews.
Co-Authors Adzhal Arwani Mahfudh Adzhal Arwani Mahfudh Adzhal Arwani Mahfudh Alfian Hidayat Ali, Thooriq Nur Azziizah, Almira Farradinda Danita Divka Sajmira Divana Taricha Salmalina Fauzan, Roman Fayza Nayla Riyana Putri I Gede Wyana Lokantara Imtias, Muhamad Bunan Indra Abdam Muwakhid Khoirul Adib Khothibul Umam Kustomo Lulu Choirun Nisa Mahfudh, Adzhal Arwani Masuzzahra, Tsaura Rafah Masy Ari Ulinuha Maya Rini Handayani Nur Cahyo Hendro Wibowo Nur Khasanah Nuurun Najmi Qonita Putri, Amiliya Putri, Indira Alifia Roman Fauzan Salsabila, Yusrina Ilmi Stefanus Widhiatmoko Subowo, Moh Hadi Syaiful Bakhri Syaiful Bakhri Syaiful Bakhri Thooriq Nur Ali