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All Journal TEKNIK INFORMATIKA Sainteks Jurnal Pseudocode SMATIKA Jurnal Ilmiah KOMPUTASI Jurnal Informatika JURNAL MEDIA INFORMATIKA BUDIDARMA InComTech: Jurnal Telekomunikasi dan Komputer Jurnal SOLMA Prosiding Seminar Nasional Teknoka Dinamisia: Jurnal Pengabdian Kepada Masyarakat SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan Jurnal Sisfokom (Sistem Informasi dan Komputer) IJID (International Journal on Informatics for Development) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal Linguistik Komputasional Jurnal ICT : Information Communication & Technology Building of Informatics, Technology and Science FIKROH: JURNAL PEMIKIRAN DAN PENDIDIKAN ISLAM Jurnal Teknologi Dan Sistem Informasi Bisnis Zonasi: Jurnal Sistem Informasi Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Asiimetrik: Jurnal Ilmiah Rekayasa Dan Inovasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Sistem Komputer dan Informatika (JSON) Infotech: Journal of Technology Information Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Teknik Informatika (JUTIF) KLIK: Kajian Ilmiah Informatika dan Komputer Sibatik Journal : Jurnal Ilmiah Bidang Sosial, Ekonomi, Budaya, Teknologi, Dan Pendidikan Jurnal Penyuuhan dan Pemberdayaan Masyarakat (JPPM) STORAGE: Jurnal Ilmiah Teknik dan Ilmu Komputer SmartComp Fikroh: Jurnal Pemikiran dan Pendidikan Islam JURNAL TEKNIK INFORMATIKA DAN KOMPUTER Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) SAMBARA: Jurnal Pengabdian Kepada Masyarakat Cosmic Jurnal Teknik BHAKTI JIVANA Smatika Jurnal : STIKI Informatika Jurnal
Firman Noor Hasan
Universitas Muhammadiyah Prof. Dr. Hamka
Religion Agriculture, Biological Sciences & Forestry 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 Engineering Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Rancang Bangun Sistem Informasi Akademik Siswa Berbasis Web: (STUDI KASUS : SMPN 103 JAKARTA) Pamungkas, Dimas; Noor Hasan, Firman
Cosmic Jurnal Teknik Vol 2 No 1 (2025): Februari
Publisher : Ali Institute or Research and Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55537/cosmic.v2i1.1073

Abstract

SMPN 103 Jakarta merupakan sebuah sekolah yang sudah berdiri sejak tahun 1974 dan mulai beroperasional pada tahun 1978, yang beralamat di Jl. RA.Fadillah Komplek Kopassus, kelurahan cipayung, kecamatan Pasar Rebo, pada saat pihak masih menggunakan cara manual dalam melakukkan input data absensi dan data absensi serta data nilai masih belum terdigitalisasi dengan baik sehingga menyulitkan guru dalam melakukan monitoring siswa bedasarkan data absensi dan data nilai, maka tujuan dari penelitian ini adalah merancang aplikasi sistem informasi akademik siswa berbasis web yang dapat dgunakan guru untuk melakkukan monitoring siswan, dalam membangun sistem ini peneliti menggunakan metode prototyping, dengan bahasa pemrograaman PHP, dan framework Laravel. Hasil dari penelitian ini sebuah aplikasi sistem informasi akademik siswa yang mudah digunakan dalam penggunaan aplikasi.
Analisis Sentimen Tanggapan Masyarakat Terhadap Penutupan TikTok Shop Menggunakan Metode Naïve Bayes Fadli, Khairul; Hasan, Firman Noor
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.6060

Abstract

E-commerce has become an important role in driving the economy, many business actors, especially MSMEs, depend on selling their products online. E-commerce continues to experience extraordinary growth, many breakthroughs have been made to improve its systems and services. One of them is social commerce which utilizes social interactions from social network users, for example TikTok Shop. However, the government changed regulations regarding social commerce which resulted in the closure of TikTok Shop operations in Indonesia, which many reactions and opinions from the public. Therefore this research was conducted with aim of analyzing public sentiment towards the closure of TikTok Shop. This research uses 1233 data taken and collected from social media X in the range September to December 2023. This research also uses the Naïve Bayes Classifier algorithm method with a training data to test data ratio of 80:20. This research resulted in accuracy of 90,24%, precision of 74,33%, and recall of 100%. The large number of negative sentiments in this sentiment analysis shows the public’s disappointment with the policy changes carried out by the government which resulted in the closure of TikTok Shop operations in Indonesia.
Utilization of the FP-Growth Algorithm on MSME Transaction Data:Recommendations for Small Gifts from The Padang Region Firman Noor Hasan; Riyan Ariyansah
JURNAL TEKNIK INFORMATIKA Vol 17, No 1: JURNAL TEKNIK INFORMATIKA
Publisher : Department of Informatics, Universitas Islam Negeri Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/jti.v17i1.37966

Abstract

The existence of adequate transaction data turns out to have a similar sales transaction pattern for MSMEs, so it would be a shame if it were left like that. Moreover, this data can be used to increase efficiency in MSMEs in the culinary sector, one of which is as a recommendation for small gifts. The study uses the Association Rules technique, whereas fp-growth is used to obtain a combination of elements. The goal is to facilitate MSMEs' ability to suggest small gifts to clients. The fp-growth algorithm calculation was implemented to process 2043 data originating from transaction data in MSMEs, with the specified minimum support value being 15%, while the minimum confidence value determined was 55%. The results of the trial obtained the two best rules, namely, "If a customer buys a list of small gifts from Balado Sanjai Chips, then the customer will buy Jangek Crackers" and "If a customer buys Jangek Crackers, then the customer will buy Sanjai Balado Chips".
Analisa Kinerja Algoritma Random Forest dan XGBoost dalam Klasifikasi Penyakit Cacar Monyet (Monkeypox) Krisna, Mohammad Dito Dwi; Hasan, Firman Noor
Journal of Information System Research (JOSH) Vol 6 No 3 (2025): April 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i3.7167

Abstract

Monkeypox is a contagious disease that requires prompt and accurate handling, particularly in the diagnostic process. However, identifying symptomps manually often takes time and is prone to error. In response to this challenge, this study aims to develop a machine learning based classification model to support a more efficient diagnosis process. This research applies two machine learning algorithms XGBoost regression and Random Forest regression to classify patients as infected or uninfected with monkeypox based on clinical symptoms. The study focuses on assessing how well each algorithm can distinguish between positive and negative cases, especially when dealing with imbalanced data or overlapping features. The dataset used consists of 25.000 entries sourced from Kaggle, each containing clinical indicators related to monkeypox. Before modeling, the data underwent exploratory data analysis (EDA) and preprocessing, including handling missing values. Furthermore, cross-validation and parameter tuning techniques were implemented to optimize model performance. The results indicate that XGBoost outperformed Random Forest, achieving 68% accuracy, 69% precision, 89% recall, and a 78% F1-score. In contrast, Random Forest yielded slightly lower scores. Both models were evaluated using the ROC curve, where each reached an AUC values of 0.60. This suggests that wile both models show potential, their ability to clearly distinguish between classes positive and negative remains limited and can be improves in future work.
Penerapan FP-Growth dan Random Forest dalam Analisis Data Penjualan Makanan Ringan Afandi, Irfan Ricky; Wahyuningtyas, Irma; Fathurrohman, Sewin; Hasan, Firman Noor
InComTech : Jurnal Telekomunikasi dan Komputer Vol 15, No 1 (2025)
Publisher : Department of Electrical Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22441/incomtech.v15i1.30260

Abstract

Penelitian ini bertujuan untuk menganalisis pola pembelian produk makanan ringan serta memprediksi penjualan produk dengan menggunakan pendekatan data mining dan machine learning. Dalam industri makanan ringan yang semakin kompetitif pemahaman mendalam tentang pola perilaku konsumen dan tren penjualan produk sangat penting untuk pengambilan keputusan bisnis yang lebih efektif serta peningkatan profitabilitas perusahaan. Tantangan utama dalam penelitian ini adalah mengidentifikasi variabel yang relevan dalam dataset penjualan untuk mengungkap pola asosiasi antar produk dan menghasilkan prediksi penjualan yang akurat. Metodologi yang digunakan dalam penelitian ini melibatkan algoritma FP-Growth untuk menemukan asosiasi produk yang sering dibeli bersamaan serta algoritma Random Forest untuk memprediksi penjualan berdasarkan data historis. Hasil penelitian dari penerapan algoritma FP-Growth mampu mengidentifikasi sembilan aturan asosiasi yang potensial untuk diterapkan dalam sistem rekomendasi produk untuk menyediakan rekomendasi produk yang lebih personal kepada konsumen. Selain itu, model prediksi menggunakan Random Forest menunjukkan performa yang baik dengan nilai Mean Absolute Error (MAE) sebesar 23,54, Root Mean Squared Error (RMSE) sebesar 36,36 dan R-squared sebesar 0,86 dengan keseluruhan menunjukkan tingkat akurasi yang cukup baik. Penelitian ini memberikan kontribusi penting dalam optimasi stok dan strategi pemasaran berbasis data. Penelitian lanjutan disarankan menggunakan data yang lebih bervariasi dan periode waktu yang lebih panjang untuk meningkatkan akurasi prediksi.
PEMANFAATAN APPSHEET UNTUK IMPLEMENTASI QR CODE PADA PENCATATAN PERKEMBANGAN SISWA TK BERBASIS MOBILE APPLICATION Avorizano, Arry; Afnan Sabili, Dian Ainurrafik; Hasan, Firman Noor
Infotech: Journal of Technology Information Vol 11, No 1 (2025): JUNI
Publisher : ISTEK WIDURI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37365/jti.v11i1.349

Abstract

The mobile application-based student progress recording application at RA Mutiara is a tool that can assist in managing student attendance and assessments, making academic data management easier. With this application, data processing becomes organized and easily accessible, the application can only be accessed by teachers. In designing the mobile application-based student progress recording application, we utilize QR Code technology as a tool to assist in student attendance and assessments. The application development is carried out using the waterfall method, which consists of five stages: needs analysis, application design, application development, and application testing. The software used in building this application includes AppSheet, Draw.io, Spreadsheet, and Google Chrome. In system testing, the method used is Black Box Testing. Based on the research results, a mobile application-based student progress recording application has been developed at RA Mutiara. From the results of the system testing questionnaire, data was obtained showing that 84.6% of the 3 users stated that the system is effective and suitable for implementation at RA Mutiara.
Pemberdayaan Literasi dan Numerasi Dasar bagi Pelajar di Kampung Gandrung Purnamaningsih, Ine Rahayu; Mayangsari, Dewi; Nugroho, Rico Setyo; Hasan, Firman Noor
Jurnal Penyuluhan dan Pemberdayaan Masyarakat Vol. 4 No. 3 (2025): Jurnal Penyuluhan dan Pemberdayaan Masyarakat (September)
Publisher : CV. Era Digital Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59066/jppm.v4i3.1681

Abstract

Kegiatan pengabdian kepada masyarakat ini berfokus pada Pemberdayaan Literasi dan Numerasi bagi anak-anak di TK Spacetoon, Desa Jambudipa, Kabupaten Bandung Barat. Kegiatan ini bertujuan untuk meningkatkan kompetensi literasi dan numerasi dasar anak usia dini melalui metode pembelajaran yang inovatif, interaktif, dan terintegrasi dengan budaya lokal. Pelaksanaan PKM berlangsung dari 18 Agustus hingga 12 September 2025, bertepatan dengan perayaan Hari Ulang Tahun Kemerdekaan Republik Indonesia. Metode yang digunakan adalah kolaborasi multi-pihak, melibatkan akademisi dari Forum Komunikasi Dosen (FKD) sebagai narasumber, serta partisipasi aktif dari seluruh komunitas sekolah yakni kepala sekolah, guru, komite, orang tua, dan tokoh masyarakat setempat (RT/RW). Hasil kegiatan menunjukkan peningkatan signifikan pada antusiasme dan kemampuan dasar literasi-numerasi anak, serta peningkatan kapasitas guru dan orang tua dalam mendampingi proses belajar anak. Program ini membuktikan bahwa sinergi antara akademisi dan komunitas dapat menciptakan model pendidikan holistik yang berkelanjutan, menumbuhkan fondasi belajar yang kuat dan relevan dengan konteks sosial budaya anak.
Analisis Sentiment Ulasan Aplikasi Riliv di Google Playstore dengan Algoritma SVM Andriani, Vivi; Hasan, Firman Noor
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7860

Abstract

This study aims to conduct sentiment analysis on user reviews of the Riliv: Mental Health App on Google Play Store using the Support Vector Machine (SVM) algorithm. The analysis process includes review data collection via web scraping, text cleaning using text preprocessing, automatic labeling based on rating scores, data transformation using the TF-IDF method, data splitting with Stratified K-Fold Cross Validation, SVM model training, and performance evaluation. The dataset comprises 2,000 reviews with an imbalanced label distribution: positive (75,3%), netral (5,3%), and negative (19,4%). The classification results show that the SVM model achieved an accuracy of 85.56%. It performed well in identifying positive sentiment with an f1-score of 0.96 and negative sentiment with 0.69. However, the model failed to classify neutral sentiment due to the small number of data, which was insufficient for meaningful pattern recognition. Evaluation and visualization results indicate that label imbalance is a major challenge. Therefore, additional strategies such as data balancing, class weighting, or the use of alternative algorithms are necessary. This research is expected to serve as a foundation for developing a more accurate and fair sentiment analysis system across all sentiment categories in the context of digital mental health services.
Penerapan Naïve Bayes untuk Mengklasifikasikan Sentimen Tidak Seimbang pada Ulasan Aplikasi Berbasis Etika Konsumen Lingga, Lingga; Hasan, Firman Noor
Journal of Information System Research (JOSH) Vol 6 No 4 (2025): July 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i4.7867

Abstract

This study aims to classify user sentiment toward an ethics-based consumption application using the Multinomial Naïve Bayes algorithm. The application examined contains social and moral content, often provoking complex opinion expressions. A total of 2,000 user reviews were collected from Google Play Store using web scraping and processed through a series of text preprocessing steps: case folding, cleansing, tokenizing, stopword removal, and stemming. The data were converted into numerical form using the Term Frequency–Inverse Document Frequency (TF-IDF) method and labeled into three sentiment categories: positive, neutral, and negative. The evaluation results show that the model achieved a precision of 92%, recall of 100%, and an f1-score of 96% for positive sentiment. However, the model underperformed in recognizing neutral and negative sentiments due to class imbalance. This study contributes to understanding the limitations of probabilistic classification models in handling imbalanced public opinion in socially driven digital spaces.
Analisis Sentimen Pengguna Aplikasi CapCut Pada Ulasan di Play Store Menggunakan Metode Naïve Bayes Meliyawati; Hasan, Firman Noor
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 4 (2024): Februari 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i4.1555

Abstract

There is an increasing interest in sharing experiences displayed in video visualizations, creating a demand for efficient and simple editing tools. CapCut is an all-in-one creative digital platform that enables video editing on browser, desktop and mobile. The CapCut app is one of the most downloaded apps on the Play Store with 500 million downloads and is available for free. CapCut app is perfect for beginner editors as it has a simple interface with various interesting features such as templates that are easy to operate without the need for additional software. However, this cannot guarantee the satisfaction of its users. Various experiences that are felt affect the assessment given by users. Sentiment analysis is important to determine the level of user satisfaction, the results of which can be used as a reference for improving the quality of the application. To find out user reviews of the CapCut application, sentiment analysis is carried out using the Naïve Bayes method with the aim of knowing the number of positive and negative sentiments from user reviews. The data used is taken from the review column available on the Play Store using web scrapping techniques with the help of Google Colab as much as 880 user review data. The data is divided into 60% training data which is 528 reviews and 40% test data which is 352 reviews. The analysis resulted in 30 more negative sentiments than positive sentiments with the number of negative sentiments totaling 455 reviews and the number of positive sentiments totaling 425 reviews. Based on the evaluation using confusion matrix, the accuracy result is 84.09%, precision is 91.91%, and recall is 73.53%.
Co-Authors Abdillah, Allif Rizki Abdul Syakir Achmad Ramadhan Achmad Sufyan Aziz Afandi, Irfan Ricky Affandi, Irfan Ricky Afikah, Prista Afnan Sabili, Dian Ainurrafik Agus Fikri Agus Fikri Ahmad Rizal Dzikrillah Ahmad Rizal Dzikrillah Ahmad Roshid Ahmad Syahril Ahmad Syahril Al Ghozi, Dhiyauddin Alfandi Safira Alim, Endy Sjaiful Allif Rizki Abdillah Allif Rizki Abdillah Allif Rizki Abdillah Ammar Rusydi Ananda Prasta Warasati Janah Ananda, Ridha Faiz Andika Saputra Andriani, Vivi Andriyani, Widyastuti Anhari, Tirta Anwar Hidayat Ari Wibowo Arief Wibowo Arien Bianingrum Rossianiz Arvin Rafialdo Aulia, Muhammad Fathan Avorizano, Arry Azhar Haikal Anwar Azhar Haikal Anwar Azis Styo Nugroho Bagas Kembar Rezkyllah Bahrul Rozak Bahrul Rozak Bisma Indrawan Dan Mugisidi Dandie Triyanto Desty Afni Dewi Mayangsari Dian Ainurrafik Afnan Sabili Dian Ainurrafik Afnan Sabili Diana Fitri Lessy Diana Fitri Lessy Dimas Febriawan Dimas Febriawan Dimas Febriawan Dion Parisda Ray Djeli Moh Yusuf Doni Gunawan Rambe E Erizal Erizal Erizal Estu Sinduningrum Estu Sinduningrum Fachri Zaini Fadli Al Gani Fadli Hardiyanto Putra Fadli, Khairul Faisal Parsakh Nursyamsi Faisal Parsakh Nursyamsyi Fajar Sidik Faldy Irwiensyah Faldy Irwiensyah Faldy Irwiensyah, Faldy Farhan Bias Purnama Putra Farhan Nufairi Farhan Nufairi Fathurrohman, Sewin Fauzan Setya Ananto Fauzi Kurniawan Fayakun Kun Febriandirza, Arafat Febriawan, Dimas Gusnul Mahesa Hafizh Dhery Al Assyam Handika, Yusuf Hanif, Isa Faqihuddin Hardyatman, Intan Diah Hazbi Santoso Hibatullah Faisal Hibatullah Faisal Hibatullah Faisal Hilmi Ammar Hilmy Zhafran Muflih I Ketut Sudaryana, I Ketut Ibnu Suhada Indra Ramadhan Indra Ramadhan Indriyanti, Prastika Intania Widyaningrum Irawati Irawati Irfan Ricky Afandi Irfan Ricky Affandi Irma Wahyuningtyas Isnan Wisnu Prastiyo Kamayani, Mia kivandi Nugroho Krisna, Mohammad Dito Dwi Kurniyati Nur Lathifah Dini Rachmawati Lingga Lingga Lingga Lita Astri Pramesti Luqman Abdur Rahman Malik Lutfi Triyuli Evana Rizki Luthfi Akbar Ramadhan M. Asep Rizkiawan Meliyawati MILASARI, LISA ASTRIA Mohammad Akhdaan Juliandra Muchammad Sholeh Muchammad Sholeh Muflih, Hilmy Zhafran Muhamad Saiful Arif Muhammad Abid Fajar Muhammad Ardhi Ryan Saputra Muhammad Ghiffar Sistani Muhammad Ikhwan Muhammad Ikhwan Muhammad Rafly Al Fattah Zain Muhammad Ridwan Muhammad Rifansyah Mukti, Avis Tantra Mutiara Zahra Arifin Nanang Juhandi Hermawan Neneng Siti Maryam Nisa Qonita Rizkina Nofendri, Yos Nugroho, Dendy Aprilianto Nunik Pratiwi Oktarina Heriyani Pamungkas, Dimas Panji Islami Anakku Pavita, Rachma Pranata, Ananda Bagas Prisilia Talakua Prista Afikah Purnamaningsih, Ine Rahayu Putri, Kirana Alyssa Rafli Erlangga Rahman Malik, Luqman Abdur Rahmatullah, Ahmad Faiz Ramadhita, Nindia Fitri Ramzah, Harry Reisa Inayah Rian gustini Ridwan Bagus Andreyanto Ridwan Maulana Subekti Rika Nurhayati Riyan Ariyansah Rizki Alamsyah Rizki Kamelia Rizky Ramdhani Rosalina Rosalina Rozak, Bahrul Saputra, Ramadani Sari, Jessica Windi Sari, Laila Atikah Setiawan, Ahmat Sewin Fathurrohman Simamora, Silvia Damayanti Sinduningrum, Estu Sistani, Muhammad Ghiffar Siti Nurhaliza Sri Fitriani Sunata, Muhamad Hafidz Ardian Syahri, Alfi Tasya Rizki Salsabilla Tia Anggita Sari Transiska, Dwi Wahyu Stiyawan Wahyuningtyas, Irma Wanda Aulia Windi Al Azmi Wulandari, Sania Zahra, Khofifah Humaeroh Az Zaini, Fachri Zuhri Halim Zuhri Halim, Zuhri