Article Per Year (5 Year)
p-Index From 2021 - 2026
11.208
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IJCCS (Indonesian Journal of Computing and Cybernetics Systems) JURNAL DESTINASI PARIWISATA JOIV : International Journal on Informatics Visualization Konvergensi : Jurnal Ilmiah Ilmu Komunikasi JURNAL MEDIA INFORMATIKA BUDIDARMA ILKOM Jurnal Ilmiah JURNAL PENDIDIKAN TAMBUSAI JURNAL TEKNOLOGI DAN ILMU KOMPUTER PRIMA (JUTIKOMP) Journal of Humanities and Social Studies AL-ULUM: JURNAL SAINS DAN TEKNOLOGI Jurnal Teknologi Terpadu Jurnal Review Pendidikan dan Pengajaran (JRPP) Pantun: Jurnal Ilmiah Seni Budaya Jurnal Informasi dan Teknologi Journal of Applied Engineering and Technological Science (JAETS) Journal of Governance and Local Politics (JGLP) Jurnal Sosial Humaniora Sigli Jurnal Scientia Journal of System and Computer Engineering Gunahumas Jurnal Ilmiah Wahana Pendidikan Jurnal Informatika Terpadu Indonesian Journal of Intellectual Publication (IJI Publication) Edu Cendikia: Jurnal Ilmiah Kependidikan Global Abdimas: Jurnal Pengabdian Masyarakat Sci-Tech Journal Jurnal Ilmiah Teknik Informatika dan Komunikasi Sentra Dedikasi: Jurnal Pengabdian Masyarakat Journal Pharmacy and Application of Computer Sciences Jurnal Informatika: Jurnal Pengembangan IT Jurnal Kajian Pendidikan dan Psikologi Toplama Jurnal Pembelajaran Bahasa dan Sastra Advances in Computer System Innovation Journal (ACSI Journal) PESHUM Indonesian Journal of Intellectual Publication (IJI Publication)
Firman Aziz
Universitas Pancasakti Makassar
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Public Health Social Sciences Transportation Other

Published : 91 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 91 Documents
Search

 6   7   8   9   10 
Implementasi Sistem Informasi Sekolah Dasar Menggunakan Metodologi Rapid Application Development (RAD): Studi Kasus di SD Sekolah Dasar Inpres Bangkala 3 Rijal, Muhammad; Istiqamah, Nurul; Aziz, Firman
Indonesian Journal of Intellectual Publication Vol. 4 No. 2 (2024): Maret 2024, IJI Publication
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/ijipublication.v4i2.502

Abstract

Sekolah Dasar Inpres Bangkala 3 adalah salah satu lembaga pendidikan dasar yang terletak di Kota Makassar, Penyampaian informasi secara cepat dan efektif dalam era modern saat ini menjadi tantangan karena umumnya dalam penyebaran informasi  masih mengandalkan metode tradisional seperti spanduk, yang memerlukan waktu dan biaya yang cukup besar. Penelitian ini bertujuan untuk membuat sebuah website Sekolah Dasar yang menarik dan mudah diakses melalui internet. Metode penelitian yang digunakan adalah Rapid Application Development (RAD), yang mencakup studi pustaka, observasi, analisis dan desain, pembuatan website, serta implementasi. Hasil penelitian menunjukkan bahwa website ini mampu mempercepat penyebaran informasi, mengurangi biaya operasional, dan memperluas jangkauan audiens. Dengan demikian, Sekolah Dasar Inpres Bangkala 3 dapat lebih dikenal oleh masyarakat luas dan informasi mengenai sekolah dapat diakses dengan lebih lengkap dan jelas. Segala informasi dari sekolah dapat lebih mudah disosialisasikan secara melalui platform digital ini.
Pengembangan Platform Kampanye Digital Interaktif Menggunakan Metode Rapid Application Development: Studi Kasus relawandj.id Rijal, Muhammad; Aziz, Firman; Lestari, Putri Ayu
Indonesian Journal of Intellectual Publication Vol. 5 No. 1 (2024): Nopember 2024, IJI Publication
Publisher : Unit Publikasi Ilmiah Perkumpulan Intelektual Madani Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51577/ijipublication.v5i1.597

Abstract

Website relawandj.id dikembangkan sebagai platform informasi dan komunikasi untuk mendukung pencalonan Dominggus sebagai Bupati dan Jumriani sebagai Wakil Bupati Kabupaten Sarmi. Platform ini dirancang sebagai sarana kampanye politik digital yang efektif, informatif, dan mudah diakses oleh masyarakat. Penelitian ini bertujuan mengevaluasi elemen-elemen penting pada website, meliputi struktur navigasi, penggunaan plugin, desain responsif untuk tampilan mobile, serta penambahan konten seperti jadwal kampanye dalam format PDF yang sesuai regulasi Komisi Pemilihan Umum (KPU). Metode yang digunakan adalah Rapid Application Development (RAD), yang memungkinkan proses pengembangan website dilakukan secara interaktif dan dinamis dalam waktu singkat. Hasil penelitian menunjukkan bahwa penggunaan plugin, seperti 3D Flipbook untuk menampilkan dokumen PDF, All in One SEO untuk optimasi mesin pencari, dan Colibri Page Builder untuk meningkatkan antarmuka pengguna, memberikan pengalaman yang lebih baik bagi pengunjung. Selain itu, desain responsif terbukti memudahkan akses melalui perangkat mobile, sehingga memperluas jangkauan kampanye dan meningkatkan keterlibatan masyarakat.
Fine-Tuning Whisper Model for Mandar Speech Recognition: Approach and Performance Evaluation Jafar, Jafar; Tb, Mar Athul Wazithah; Aziz, Firman; Iriany, Rosary; Nasir, Norma
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 1 (2025): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i1.7170

Abstract

This research focuses on the development of speech recognition technology for the Mandar language, a regional language in Indonesia with limited digital resources. The main challenge lies in the lack of local datasets and the minimal representation of the Mandar language in existing multilingual speech recognition models. This study aims to enhance the performance of Automatic Speech Recognition (ASR) systems by fine-tuning the Whisper model using a Mandar-specific dataset. The dataset consists of 1,000 audio recordings with various dialects and recording qualities, which underwent preprocessing steps such as segmentation, normalization, and data augmentation. Fine-tuning was conducted using supervised learning methods with hyperparameter optimization, resulting in a reduction of Word Error Rate (WER) from 73.7% in the pretrained model to 37.4% after fine-tuning, and an increase in accuracy from 26.3% to 62.6%. The optimized model was also compared with other ASR models, such as DeepSpeech and wav2vec 2.0, demonstrating superior performance in terms of accuracy and time efficiency. Further analysis revealed that recording quality and dialect variations significantly impacted model performance, with high-quality recordings and standard dialects yielding the best results. The model was implemented as a web application prototype, enabling efficient and near real-time transcription of Mandar speech. This research not only contributes to the development of ASR technology for low-resource languages but also opens new opportunities for preserving and utilizing the Mandar language through digital technology. For future improvements, larger datasets, more advanced augmentation techniques, and the exploration of additional language model integration are recommended.
Persepsi Pengguna Rokok Elektrik Di Kalangan Mahasiswa Teknologi Pangan Universitas Pendidikan Indonesia: Studi Deskriptif Kualitatif Mahdia, Naila Maulida; Aziz, Firman; Ferdiana, Ryan; Aqdami, Nashrullah Tsabbit; Putrinima, Ayudia Qoryn; Sazeli, Aulya Sasikirana; Cahya, Nayla Riskia; Firdaus, Siti Laya Nurbaiti
Jurnal Ilmiah Wahana Pendidikan Vol 11 No 12.D (2025): Jurnal Ilmiah Wahana Pendidikan
Publisher : Peneliti.net

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Rokok elektrik atau vape semakin populer sebagai alternatif rokok konvensional, khususnya di kalangan mahasiswa. Meskipun dianggap lebih aman, berbagai penelitian menunjukkan bahwa vape tetap mengandung senyawa berbahaya yang berpotensi menimbulkan gangguan kesehatan, seperti inflamasi saluran pernapasan dan ketergantungan nikotin. Tingginya prevalensi pengguna vape di Indonesia menimbulkan kekhawatiran akan dampak jangka panjangnya, terutama di tengah minimnya pemahaman masyarakat terhadap risiko yang ditimbulkan. Penelitian ini bertujuan untuk mengetahui persepsi penggunaan rokok elektrik oleh Mahasiswa Teknologi Pangan di Universitas Pendidikan Indonesia. Penelitian ini menggunakan pendekatan kualitatif dengan metode purposive sampling dengan melibatkan lima mahasiswa Program Studi Teknologi Pangan di Universitas Pendidikan Indonesia yang merupakan pengguna rokok elektrik. Data dikumpulkan melalui kuesioner online berisi pertanyaan terbuka berbasis kerangka Health Belief Model, lalu dianalisis menggunakan teknik analisis tematik untuk mengidentifikasi pola persepsi terhadap risiko kesehatan. Hasil penelitian menunjukkan bahwa mahasiswa pengguna rokok elektrik memiliki persepsi yang beragam terkait aspek keamanan, ketergantungan, dan risiko kesehatan. Motivasi penggunaan umumnya didorong oleh faktor psikologis dan sosial, meskipun terdapat ambivalensi antara kesadaran risiko dan kenyamanan dalam penggunaannya. Oleh karena itu, diperlukan upaya edukasi berkelanjutan dan kebijakan pengendalian yang berbasis bukti untuk meningkatkan kesadaran risiko dan mengurangi penggunaan rokok elektrik di kalangan mahasiswa.
Detection of Persistent vs. Non-Persistent Drugs in Pharmacy Using Decision Tree Classification Based on Gini, Entropy, and Log Loss Criteria Mardewi, Mardewi; Aziz, Firman; Usman, Syahrul; Fuadi Syam, Rahmat
ILKOM Jurnal Ilmiah Vol 17, No 2 (2025)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v17i2.2585.186-195

Abstract

This study evaluates the performance of Decision Tree methods in classification, utilizing three different criteria: Entropy, Gini, and Log Loss. The objective is to determine which criterion is most effective in achieving high classification accuracy using prescription data from the UCI repository, comprising 3,424 prescription records with 67 variables. The analysis results show that the Entropy criterion delivers the best performance with an accuracy of 79.1%, followed by the Gini criterion at 78%, and the Log Loss criterion at 77.9%. These findings indicate that the Entropy criterion is superior in reducing uncertainty and capturing the underlying data structure, while both Gini and Log Loss criteria also provide competitive, though slightly lower, results. The main contribution of this research is a comparative evaluation of decision tree criteria using real-world prescription data to support accurate classification of medication adherence, which can be beneficial for developing intelligent pharmacy systems. This research offers valuable insights into the effectiveness of various criteria within the Decision Tree method and can aid in selecting the most appropriate criterion for future classification applications.
Classification of Multiclass Ensemble SVM for Human Activities based on Sensor Accelerometer and Gyroscope Wungo, Supriyadi La; Mardewi, Mardewi; Aziz, Firman; Ishak, Pertiwi; SHILI, Hechmi
ILKOM Jurnal Ilmiah Vol 15, No 1 (2023)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v15i1.1270.107-117

Abstract

Human Activity Recognition is technology introduced to recognize human activities. Several technologies that have been applied are Accelerometer sensors, Gyroscope sensors, Cameras, and GPS. The selection of the Support Vector Machine algorithm is due to its capabilities to minimize errors in training data sets and the Curse of dimensionality which can estimate parameters as well as its ability to find the best hyperplane that separates two classes. The SVM algorithm was originally developed for the classification of two classes. Problem raised if there are more than two classes. In addition, the performance will not optimal for the large-scale data. Therefore, modification the current design is needed. An ensemble technique can be used to combine the Support Vector Machine algorithm with the bagging algorithm. This study proposes the application of an ensemble SVM algorithm to classify human activities based on accelerometers and gyroscope sensors on smartphones.  The total data is 13725 records with 4575 representatives of each class. From the results of the overall data partition carried out in the calcification process using the ensemble SVM algorithm, the best performance was generated when comparing datasets with 80% training data and 20% test data from a total of 13725 records because it succeeded in increasing accuracy, precision, and sensitivity.
Spatio-Temporal Graph Neural Network Based on Nonlinear Time–Frequency Features for Mu-ERD Classification in Multi-Session EEG Motor Imagery Firman Aziz; Jeffry Jeffry; Syahrul Usman; Rahmat Fuadi Syam; Muhammad Nur Arafah; Nurul Fathanah Mustamin
Journal of Applied Engineering and Technological Science (JAETS) Vol. 7 No. 2 (2026): Journal of Applied Engineering and Technological Science (JAETS)
Publisher : Yayasan Riset dan Pengembangan Intelektual (YRPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37385/jaets.v7i2.8679

Abstract

Mu rhythm event-related desynchronization (ERD) is a key indicator of motor imagery activity based on EEG signals. However, accurate classification of ERD remains challenging due to the nonlinear nature of EEG signals and inter-session variability. This study proposes a motor imagery classification approach using a Spatio-Temporal Graph Neural Network (ST-GNN) model that leverages nonlinear time-frequency features extracted via Variational Mode Decomposition (VMD) and Synchrosqueezing Transform (SST). The dataset was collected from a single healthy subject across five separate sessions, each consisting of two conditions: relaxation and motor imagery. After preprocessing and segmentation, features were extracted and represented as spatio-temporal graphs to be processed by the ST-GNN. The model was evaluated using metrics such as accuracy, F1-score, AUC-ROC, and the Session Stability Index (SSI). The results show that the ST-GNN achieved an accuracy of 94.2%, F1-score of 94.1%, and AUC-ROC of 96.1%, along with high prediction stability across sessions. This performance outperformed baseline models including CNN, CSP+SVM, and STFT+MLP.These findings support the hypothesis that ERD is a distributed brain network phenomenon and demonstrate that the ST-GNN approach with VMD/SST-derived features is a promising strategy for developing adaptive and accurate BCI systems.
Sistem Pendukung Keputusan Penentuan Destinasi Objek Wisata Dengan Metode Simple Additive Weighting (SAW) Berbasis Web Jeffry jeffry; firman aziz; syahrul usman
Journal of System and Computer Engineering Vol 5 No 2 (2024): JSCE: Juli 2024
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v5i2.1339

Abstract

One of the biggest regional proceeds of the North Toraja Regency comes from the utilization of tourist objects as recreational objects whether for the local communities or the overseas. However, the lack of information and the lack of systems technology in Toraja destination caused many tourists to visited a few of the many tourism objects available. This problem causes tourists to tend to visit only a fraction of the many tourism objects. Based on these problems, we need a system that helps provide information and determine tourist objects suitable for each tourist, and the tour is more varied. This study produces a decision support system for selecting tourism objects in North Toraja using the “Simple Additive Weighting” method based on a website in the goal of assisting tourists to determine tourist place
Sentiment Analysis of Indonesian Government Policies Using the LSTM Model for Public Opinion Mapping Muhammad Rijal; Firman Aziz; Nuzul Tenriana; Eva Delilah
Jurnal Pemerintahan dan Politik Lokal Vol 8 No 1 (2026): JGLP, MAY 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47650/jglp.v8i1.2336

Abstract

Social media has evolved into a primary arena for citizens to express and negotiate opinions regarding government policies, creating vast opportunities for data-driven policy evaluation. This study aims to map public sentiment toward Indonesian government policies by integrating deep learning–based sentiment classification with linguistic and governance analysis. A dataset of approximately 50,000 Indonesian-language posts was collected from Twitter (X) and Facebook between January and June 2024. The data were processed through text cleaning, tokenization, stopword removal, and word embedding using Word2Vec and FastText, and subsequently classified into positive, negative, and neutral sentiments using a Long Short-Term Memory (LSTM) model. The results indicate that public opinion is predominantly negative (45%), particularly in relation to economic and taxation policies, while positive sentiment (34%) is mainly associated with education and health sectors. The LSTM model achieved an accuracy of 86.9%, outperforming Support Vector Machine (SVM) and Naïve Bayes models. Furthermore, linguistic analysis reveals that emotive and sarcastic expressions play a significant role in shaping critical public discourse, whereas colloquial language enhances engagement, especially among younger users. This study contributes by bridging computational sentiment analysis with linguistic interpretation and public policy evaluation within a unified framework. The findings provide practical implications for evidence-based policymaking by enabling governments to monitor public sentiment in real time, improve policy communication strategies, and foster more participatory and responsive governance.
Sentiment Analysis of Government Policies Using LSTM: The Role of the Indonesian Language in Shaping Public Opinions Eva Delilah; Rahmat Fuady Syam; Firman Aziz
Journal of System and Computer Engineering Vol 7 No 1 (2026): JSCE: January 2026
Publisher : Universitas Pancasakti

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61628/jsce.v7i1.2574

Abstract

Social media has become the primary arena for the public to express opinions on government policies. This study aims to analyze public sentiment toward government policies using the Long Short-Term Memory (LSTM) model, while also examining the role of language in shaping public opinion. Data were collected from social media posts related to economic, social, and health policies, followed by preprocessing stages including text cleaning, tokenization, stopword removal, and word embedding with Word2Vec. The LSTM model was compared with Support Vector Machine (SVM) and Naïve Bayes to evaluate accuracy and performance. The results indicate that public opinion is dominated by negative sentiment (45%), particularly regarding economic policies. The LSTM model outperformed the benchmarks with an accuracy of 86.9%, surpassing SVM and Naïve Bayes. Linguistic analysis revealed the frequent use of emotional diction, sarcasm, and economic burden narratives that reinforced public resistance, while colloquial language was found to be an effective tool for engaging younger generations. This study contributes to the advancement of sentiment analysis in the Indonesian language using deep learning and provides practical recommendations for policymakers to design more persuasive and participatory communication strategies.
Co-Authors A Inayah Auliyah Abasa, Sustrin Achmad Hufad Adriana, Andi Nur Ilmi Adriana, Andi Nurilmi Afifah, Mira Aulia Ahmad Sukarna Syahrir, Ahmad Sukarna Akbar Taufik Almuhajir Haris, Almuhajir Amalyah, Aam Amelia, Kiki Resqy Ampauleng Ampauleng Andi Nurilmi Adriana Andi Taufiqurrahman Akbar Andjani, Andita Dwi Andri Kurniawan Andyka Andyka, Andyka Anirwan Anirwan Annisa Sakanti Tamir Annisa Salsabila Apriliya Wijaya Anugriaty Indah Asmarany Aqdami, Nashrullah Tsabbit Arafah, Muhammad Nur Areta Nararya Putri Setiadi Arifin, Syaadiah Armansyah, M Rezky Armin Lawi Arni, Sitti Artikasari, Devina Arvito, Djendral Muhammad Ayu Asrhi, Nur Ayu, Rizkia Siva Aziz, Naufal Nuurul Aziz Azminuddin I. S. Azis Barokah, Nurul Nur Batau, Radus Buang, Ariyani Buang, Misbahuddin Buyung Firmansyah Cahya, Nayla Riskia Delilah, Eva Dessy Putri Wahyuningtyas Dhilan Sasmita Enal Wahyudi, Abdi Eva Delilah Eva Delilah Fadhila Amri, Nur Faisal Rahman Fajriana, Fajriana Fani Temarwut, Farid Fatimah Azzahra NF Ferdiana, Ryan Fiina Lanahdiyan Najah Firdaus, Siti Laya Nurbaiti Firmansyah Firmansyah Firmansyah Firmansyah, Arya Pramudya Fuadi Syam, Rahmat Fujiono, Fujiono Gunawan, Resky Nuralisa H, Rezha Ilma Hafsah, Hafni Hamdani Nur, Nur Hanayanti, Citra Siwi Hanum Nur Alifia Hasriani Hasriani, Hasriani Hayati, Ristia Nur Hikam, Zaki Maula Hilyah, Finan Azka Nuzilla Indrayani, Lilis Intan, Dyah Noor Iriany, Rosary Irmawati Irmawati Ishak, Pertiwi Iskandar, Imran Ismail Ismail Istiqamah, Nurul Jafar Jafar Jafar Jafar Jeffry Jeffry Jeffry Kahar Gani Khairunnisa, Salwa Kurniyan Sari, Sri Kusumawardhani, Anggun L.E.P, Benny La Wungo, Supriyadi Lempi, Herga Andar Lutfi Budi Ilmawan, Lutfi Budi M Rezky Armansyah Mahdia, Naila Maulida Manan, Linda Ifni Pratiwi Marcelina, Dona Mardewi, Mardewi Marzuki Maulani, Rista Nabilah Merdewiningsi, Andi Mindra, Davin Septian Misbah Abdul Aziz Muhammad Arfah Asis Muhammad Lutfi Muhammad Rijal Muhammad Rijal Muhammad Rijal Mutia Maulida Nasir, Norma Nasruddin Nasruddin Nur Ayu Asrhi Nur Ayu Asrhi Nur Hamadani Nur Nur Hamdani Nur Nur, Nur Hamdani Nurafni Shahnyb Nurafni Shahnyb Nurdyansa Nurul Fathanah Mustamin Nurul Fathanah Mustamin Nurul Istiqamah Nuzul Tenriana Osman, Isnawati Panggabean, Benny Leonard Enrico Paramitha, Aura Rahma Priambodo, Caka Gatot Putri Ayu Lestari Putrinima, Ayudia Qoryn Qamal Qamal Rahma, Nabila Nailatur Rahma, Widya Rahmania Nur Saputra Rahmat Fuadi Syam Rahmat Fuady Syam Reinata, Vanya Fara Restu Arsyana Rijal, Muhammad Rizqya Aufa Nuraini Rofi’i, Agus Rohmah Nur Hidayah Ronald Yehezkiel Sitompul Ryan Ferdiana Sari, Sri Kurniyan Satar Satar Sazeli, Aulya Sasikirana Sembiring, Darmawanta Shahnyb, Nurafni Shavi Khalwa Khalisha Shili, Hechmi Siti Saidah Soeriakartalegawa, Aldo Pranata Sofyan Sofyan Sudarmadi Putra Sumardi . Sumardi Sumardi Suroso Suroso Syahrul Usman Syam, Rahmat Fuadi Syam, Rahmat Fuady Tanniewa, Adam M Taufik , Akbar Taufik, Akbar Tazkillah, Ghina Ajmal Tb, Mar Athul Wazithah Triani, Novita Trianita, Desi Umar, Hendra Velayaty, Ali Akbar Vismania S. Damaianti, Vismania S. Wahab, Andyka Wahyudi, Andi Enal Wiftasya, Najla Wijaya, Neti Septi Wulandari, Ayu Ratna Wungo, Supriyadi La Yahya, Kurnia Yance Manoppo Yarkuran, Nuru Zahra Hasna Nabilla Zahra, Agifa Faiza Zhafira Tsania Rasyiffah Zulkarnain Zulkarnain