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All Journal MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JISICOM (Journal of Information System, Infomatics and Computing) Jurnal Teknologi Informasi dan Multimedia Jurnal Informatika dan Rekayasa Elektronik Jurnal Varian Jurnal Bumigora Information Technology (BITe) SOLID Jurnal Teknimedia: Teknologi Informasi dan Multimedia Jurnal Kependidikan: Jurnal Hasil Penelitian dan Kajian Kepustakaan di Bidang Pendidikan, Pengajaran dan Pembelajaran Djtechno: Jurnal Teknologi Informasi Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Artificial Intelligence and Digital Business Bakti Sekawan : Jurnal Pengabdian Masyarakat Prosiding Seminar Nasional CORISINDO
Azhar, Raisul
Program Studi Ilmu Komputer, Universitas Bumigora
Religion Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Social Sciences Other

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Assessing Twitter User Sentiment Regarding Divorce Issues Using the Random Forest Method Muhamad Azwar; I Putu Hariyadi; Raisul Azhar
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 4 No. 2 (2025): September 2025
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v4i2.4980

Abstract

The issue of divorce remains a complex and sensitive topic within Indonesian society, influenced by various factors such as repeated disputes, domestic violence, lack of harmony, financial difficulties, and other socio-cultural aspects. With the rise of social media, particularly Twitter, public discussions regarding divorce have become more widespread, allowing individuals to express their opinions and sentiments on the subject. These diverse perspectives create a wealth of sentiment data that can be analyzed to understand public perception and societal trends related to divorce. This study aims to classify public sentiment on divorce-related discussions using the Random Forest algorithm, providing insight into how people perceive and react to divorce issues. The research adopts a quantitative approach with a case study framework. The methodology involves data collection through web scraping techniques to gather approximately 1500 tweets containing discussions on divorce. The collected data is then preprocessed, including text cleaning, tokenization, and feature extraction, before being used to train and evaluate the Random Forest model. Sentiments are classified into three categories: negative, neutral, and positive. The classification model's performance is assessed using accuracy and F1-score metrics derived from the confusion matrix to determine its effectiveness in categorizing sentiments. Experimental results indicate that the Random Forest algorithm achieves an accuracy of 70%. The relatively low accuracy is attributed to the imbalance in sentiment class distribution, where negative sentiments dominate while positive sentiments are underrepresented. This imbalance affects the model's ability to predict positive sentiments effectively. The implications of this research contribute to a better understanding of public sentiment dynamics regarding divorce, which can be beneficial for policymakers, psychologists, and social researchers in analyzing societal attitudes towards marital dissolution.
Klasifikasi Keparahan Kecelakaan Lalu Lintas Berbasis Decision Tree C4.5 dan Teknik SMOTE Alfiansyah, Githa; Hendra; Azhar, Raisul
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5252

Abstract

Kecelakaan lalu lintas merupakan masalah serius yang berdampak besar terhadap keselamatan masyarakat, khususnya di Provinsi Nusa Tenggara Barat (NTB). Dalam beberapa tahun terakhir, angka kecelakaan di NTB menunjukkan tren fluktuatif dengan kecenderungan peningkatan pada kategori kecelakaan berat. Kondisi ini menjadi isu penting yang harus segera mendapat perhatian dari pihak berwenang maupun masyarakat. Tingginya angka kecelakaan, terutama yang berakibat fatal, menegaskan perlunya upaya penanggulangan berbasis data yang terarah dan berkelanjutan. Dalam upaya menurunkan tingkat kecelakaan dan meningkatkan keselamatan lalu lintas, penelitian ini bertujuan untuk mengklasifikasikan tingkat keparahan kecelakaan lalu lintas menggunakan algoritma Decision Tree (C4.5) yang dikombinasikan dengan teknik SMOTE untuk mengatasi ketidakseimbangan data antar kelas. Data yang digunakan merupakan data sekunder dari Badan Pusat Statistik NTB, mencakup periode 2018 hingga 2023. Model divalidasi menggunakan 15-Fold Cross Validation dan menghasilkan akurasi sebesar 98,33%. Hasil klasifikasi menunjukkan bahwa atribut "Jumlah" merupakan variabel paling berpengaruh dalam membentuk aturan klasifikasi, dengan pembagian kategori keparahan ke dalam tiga kelas: Rendah, Sedang, dan Tinggi. Temuan ini menunjukkan bahwa model memiliki performa tinggi dan dapat dimanfaatkan sebagai alat bantu dalam menyusun kebijakan pencegahan kecelakaan, mengidentifikasi wilayah rawan, serta mendukung pengambilan keputusan yang lebih tepat dalam upaya menurunkan risiko kecelakaan di wilayah NTB.
Perancangan Prototype Sistem Pengering Kopi Dengan Metode Research and Development (RND) Berbasis Internet of Things (IOT) Sukma, I Gusti Ayu Yunita; Hadi, Sirojul; Latif, Kurniadin Abdul; Husain; Azhar, Raisul
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5279

Abstract

Perkembangan teknologi berbasis Internet of Things (IoT) dan memanfaatkan energi dari  Pembangkit Listrik Tenaga Surya (PLTS) dapat memberikan solusi inovatif dalam pengeringan biji kopi. Penelitian ini bertujuan untuk merancang dan mengembangkan sistem pengeringan biji kopi menggunakan Internet of Things dan Pembangkit Listrik Tenaga Surya (PLTS). Sistem ini juga dapat memantau suhu dan kelembaban didalam ruangan pengering biji kopi secara real-time melalui website yang berfungsi untuk meningkatkan efisiensi waktu pengeringan serta mengatasi keterbatasan metode pengeringan tradisional yang sangat bergantung pada kondisi cuaca. Metode penelitian menggunakan metode Research and Development (R&D) dan menggunakan model Borg and Gall. Sistem ini dirancang menggunakan sensor suhu dan kelembaban yaitu (DHT22) serta sensor tegangan untuk menstabilkan daya listrik dan menggunakan mikrokontroller (ESP32), output yang di keluarkan yaitu lampu pemanas dan exhaust fan. Hasil dalam penelitian ini adalah memonitoring nilai suhu dan kelembaban yang diatur suhu 30℃, dan kelembaban 50%. Hasil monitoring yang telah diperoleh selama 7 hari kurang lebih. Sistem ini juga mampu dapat melihat suhu dan kelembaban tanpa harus mengecek Kembali, dengan memanfaatkan energi dari Pembangkit Listrik Tenaga Surya (PLTS) jadi prototype ini memberikan Solusi berkelanjutan yang lebih efisien untuk pengeringan kopi, mengatasi keterbatasan pada cuaca dan juga mendukung pemantauan jarak jauh melalui website.
Implementasi Disaster Recovery melalui Backup Otomatis pada XenServer berbasis FreeNAS Ikrom, Paizul; Husain, Husain; Marzuki, Khairan; Azhar, Raisul; Hariyadi, I Putu
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5420

Abstract

Penelitian ini bertujuan untuk mengembangkan sistem disaster recovery dan backup data otomatis yang terintegrasi dengan XenServer, menggunakan FreeNAS sebagai media penyimpanan. Sistem dirancang untuk melakukan backup virtual machine (VM) secara otomatis dari XenServer ke FreeNAS Primary, dan dilanjutkan dengan replikasi ke FreeNAS Secondary sebagai cadangan tambahan. Proses backup dilakukan secara terjadwal menggunakan skrip otomatis berbasis cron dan rsync, serta ditambahkan fitur notifikasi untuk memantau keberhasilan proses backup. Implementasi dilakukan dalam beberapa tahap, mulai dari konfigurasi jaringan NFS antara XenServer dan FreeNAS, pengujian proses backup harian, replikasi data antar storage, hingga simulasi proses restore VM dari backup. Hasil pengujian menunjukkan bahwa sistem mampu melakukan backup dan replikasi secara konsisten, serta dapat melakukan pemulihan data dengan cepat ketika terjadi kerusakan atau kehilangan data di XenServer. Proses backup otomatis menggunakan cron telah terbukti berjalan secara terjadwal dan konsisten. Setiap file hasil backup disimpan dalam direktori yang sama dengan penamaan unik berdasarkan tanggal dan waktu (timestamp), sehingga tidak terjadi penimpaan antar file.
Rancang Bangun Sistem Ruang Budidaya Jamur Dengan Metode Research And Development Berbasis Internet Of Things Rana, Dwi; Hadi, Sirojul; Zulfikri, Muhammad; Husain, Husain; Azhar, Raisul
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5603

Abstract

Budidaya jamur tiram membutuhkan kondisi lingkungan optimal, seperti suhu, kelembapan, sirkulasi udara, dan pencahayaan. Pengendalian manual kurang efisien, terutama jika lokasi budidaya jauh. Penelitian ini bertujuan merancang sistem ruang budidaya jamur tiram berbasis Internet of Things (IoT) untuk pemantauan dan pengendalian otomatis. Metode yang digunakan adalah Research and Development (R&D) dengan sensor DHT22 untuk suhu dan kelembapan, MQ-135 untuk kualitas udara, serta BH1750 untuk intensitas cahaya. Mikrokontroler ESP32 digunakan sebagai pengendali utama yang terhubung ke platform website untuk pemantauan real-time. Hasil pengujian menunjukkan sistem mampu menjaga kondisi lingkungan dengan akurasi sensor suhu 99,4%, kelembapan 95,6%, dan intensitas cahaya 94,8%. Pemanfaatan Pembangkit Listrik Tenaga Surya (PLTS) meningkatkan efisiensi energi dan mendukung keberlanjutan. Sistem ini terbukti lebih efektif dibandingkan metode manual dalam meningkatkan pertumbuhan jamur tiram, sehingga menjadi solusi inovatif dalam budidaya jamur.
Peningkatan Kompetensi Guru SMAN 7 Mataram dalam Melaksanakan Pembelajaran dengan Pendekatan Deep Learning Azwar, Muhamad; Hariyadi, I Putu; Azhar, Raisul; Priyanto, Dadang; Adil, Ahmat; Santoso, Heroe; Syahrir, Moch.; Augustin, Kartarina; Zulkipli, Zulkipli; Darma, I Made Yadi; Asroni, Ondi; Qulub, Mudawil; Azhar, Lalu Zazuli; Widyawati, Lilik; Anas, Andi Sofyan
Bakti Sekawan : Jurnal Pengabdian Masyarakat Vol. 5 No. 2 (2025): Desember
Publisher : Puslitbang Sekawan Institute Nusa Tenggara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/bakwan.v5i2.852

Abstract

The capability of educators to respond to the dynamics of 21st-century education is a primary determinant in establishing a high-quality learning environment. Based on initial findings at SMAN 7 Mataram, a disparity was identified between the urgency of applying varied learning models and the reality in the field, which still relies heavily on conventional, teacher-centered approaches. This situation implies minimal active student participation and suboptimal stimulation of critical thinking skills or Higher Order Thinking Skills (HOTS). This community service program was initiated to escalate teacher capacity at SMAN 7 Mataram, specifically in designing Deep Learning-based schemes. The implementation approach adopted the Participatory Action Research (PAR) method, involving the full attention of 70 teachers through a series of phases, ranging from preparation and implementation to evaluation and mentoring. Key interventions included training on compiling Deep Learning-oriented Lesson Plans and teaching simulations. Program effectiveness was measured through questionnaires, lesson plan document reviews, and observations. Evaluation data showed a substantial positive impact, marked by an increase in conceptual understanding of Deep Learning indicators (40%), 6C principles (40%), the teacher's function as a facilitator (32%), and the application of authentic assessment (40%). In terms of implementation, the quality of lesson plans accommodating student-centered activities surged significantly from 30% in the pre-activity phase to 100% after the activity. It can be concluded that this program effectively boosts teachers' pedagogical competence comprehensively and encourages the transformation of teaching practices in the classroom to become more dynamic.
Deteksi Malware pada Perangkat Android Menggunakan Ensemble Learning Muhamad Azwar; Lilik Widyawati; Raisul Azhar; Kartarina Kartarina; Tanwir Tanwir; Andi Sofyan Anas
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 3 (2025): August
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i3.573

Abstract

The increasing use of permission-based applications on mobile platforms has raised concerns regarding privacy and security. Android, being one of the most widely used operating systems for interacting with mobile applications, is particularly susceptible to various security risks that must be promptly addressed. Low digital literacy and a lack of user awareness about security risks—especially when installing applications from unofficial sources or without paying attention to access permissions—make users vulnerable to malware attacks. Uninformed users can easily become victims of malware insertion by irresponsible parties, turning them into targets for data manipulation and even data theft, which may then be sold on illegal forums. Attackers exploit the permission system, allowing them to freely access the target smartphone. This lack of awareness among users increases their vulnerability to malware injection and subsequent threats such as data manipulation and the theft of personal information, which can be traded on underground markets. One approach to detecting malicious behavior in mobile applications is the use of machine learning techniques. These techniques can analyze application patterns and behaviors based on features such as requested permissions. Popular algorithms for malware detection include Support Vector Machine (SVM) and Random Forest (RF), both of which have demonstrated strong performance in various studies. However, to further improve accuracy and reduce classification errors, ensemble learning approaches such as Adaptive Boosting (AdaBoost) are increasingly being adopted. Ensemble learning combines multiple predictive models to produce more reliable classification results compared to single models. This study evaluates the performance of several classification algorithms in detecting malicious Android applications. The results show that AdaBoost achieved a high accuracy rate of 91.65% and an AUC value of 95%, effectively distinguishing between safe applications and malware. Therefore, the use of machine learning algorithms—particularly ensemble methods like AdaBoost—can serve as a promising solution to enhance the security and privacy of Android-based mobile application users.
Implementasi Software-Defined Network Terintegrasi Firewall pada Proxmox untuk Pengontrolan Konfigurasi Jaringan dan Pengamanan Layanan Container I Putu Hariyadi; I Made Yadi Dharma; Raisul Azhar; Suriyati Suriyati
Jurnal Teknologi Informasi dan Multimedia Vol. 7 No. 1 (2025): February
Publisher : Sekawan Institut

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35746/jtim.v7i1.644

Abstract

Virtualization technology has helped companies consolidate various server roles into a single physical server, reducing hardware costs. Hypervisor is a software in virtualization that is used to manage server hardware, allowing multiple Virtual Machines (VM)/Containers (CT) to run on a single physical machine. Companies face various challenges to remain competitive in the digital era, such as the need for rapid deployment of virtual guests and virtual networks on hypervisors in development, testing, and production environments, as well as securing network services. The purpose of this study is to implement SDN on hypervisors to centrally control virtual network configurations with a simple design, reducing setup and maintenance costs and time. In addition, it also implements a firewall and Virtual Private Network (VPN) based on OpenVPN and a reverse proxy to secure the hypervisor and VM/CT so that services remain available. This study presents a new approach that integrates Software-Defined Network (SDN)-based network management with comprehensive security solutions on hypervisors. This approach combines efficiency in network management and security that have rarely been focused on simultaneously in previous studies. The research method uses the Network Development Life Cycle (NDLC). The hypervisor used is Proxmox Virtual Environment (PVE) which is installed on the Virtual Private Server (VPS) provider IDCloudHost. Based on the results of the trials that have been carried out, it can be concluded that the simple zone type SDN on PVE can be used to control network configurations centrally and more simply such as routing, Dynamic Host Configuration Protocol (DHCP), Source Network Address Translation (SNAT), hostname registration and Internet Protocol (IP) from CT to forward lookup zone on the Domain Name System (DNS) server. Activating the firewall and creating rules at the cluster and CT levels from PVE and OpenVPN can protect the infrastructure when accessed both internally and externally. While the implementation of nginx reverse proxy can secure access to HTTP/HTTPS services on CT in PVE.
PEMILIHAN BAHAN PUPUK ORGANIK UNGGULAN DALAM SISTEM PENDUKUNG KEPUTUSAN MENGGUNAKAN METODE WEIGHTED PRODUCT Heroe Santoso; Raisul Azhar; Suriyati Suriyati; Melati Rosanensi; I Made Yadi Dharma; Husain Husain; Fathurrahman Fathurrahman
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 5 No. 2 (2024): Desember 2024
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v5i2.208

Abstract

Organic fertilizer is a type of fertilizer that comes from natural ingredients that contain organic materials, such as plant, animal or other organic waste. Organic fertilizer naturally contains essential nutrients for plants, such as nitrogen, phosphorus, potassium, micronutrients and beneficial organic materials. Organic fertilizers have experienced significant development in recent years. Increased awareness of the importance of sustainable and environmentally friendly agriculture has encouraged the use and development of organic fertilizers. Meanwhile, organic fertilizer can be produced through composting, fermentation or decomposition of organic materials. To be able to produce superior and quality fertilizer products, of course you must choose superior or quality product ingredients. Sources of organic material can be goat kohe, effective microorganism 4 (EM4), bamboo leaf waste, chicken manure waste, burnt husks, cocopeat, coconut fiber. This research aims to create a decision support system using the weighted product (WP) method. WP is a popular multi-criteria analysis decision and is a multi-criteria decision making method. The choice of the weighted product (WP) method is also based on its ability to provide optimal solutions in the ranking system. The choice of this method is also based on the computational complexity which is not too difficult so that the time required to produce calculations is relatively short
ANALISIS SENTIMENT KEUANGAN MENGGUNAKAN FINE-TUNED FINBERT Heroe Santoso; Raisul Azhar; Suryati Suryati; Melati Rosanensi; I Made Yadi Dharma; Husain Husain; Ahmat Adil; Muhamad Azwar; I Putu Hariyadi
TEKNIMEDIA: Teknologi Informasi dan Multimedia Vol. 6 No. 2 (2025): Desember 2025
Publisher : Badan Penelitian dan Pengabdian Masyarakat (BP2M) STMIK Syaikh Zainuddin NW Anjani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.46764/teknimedia.v6i2.316

Abstract

Financial information is a critical type of data for analysis. However, because much of it is unstructured and widely dispersed, an appropriate analytical method is required, one of which is sentiment analysis. In the financial context, sentiment analysis is employed by the industry to assess public perceptions of companies or market conditions. This study implements a fine-tuned FinBERT model to perform sentiment analysis in the financial sector. The dataset used is a combination of FiQA (Financial Question Answering) and The Financial PhraseBank, consisting of English sentences labeled with negative, neutral, and positive sentiments. The research process involved data preprocessing, tokenization, data splitting, model training, and evaluation using accuracy, precision, recall, F1-score, and confusion matrix metrics. The results show that the model achieved 82% accuracy, with its best performance in the positive class (F1-score 0.88) and the neutral class (F1-score 0.85), but weaker performance in detecting the negative class (F1-score 0.49). These findings indicate that the fine-tuned FinBERT is effective for financial sentiment analysis, particularly for positive and neutral sentiments, though improvements are needed in negative sentiment detection, potentially through expanding training data diversity or applying data augmentation techniques
Co-Authors Afriandi, Mahsus Ahmat Adil Al-Mu’min, Al-Mu’min Alfiansyah, Githa Andi Sofyan Anas Andi Sofyan Anas Anthony Anggrawan Apriani Apriani Ardani, Awin Hersa Arsa Wisesa, Gde Bagus Aryadita Augustin, Kartarina Azhar, Lalu Zazuli Dadang Priyanto Daniel Happy Putra Darma, I Made Yadi Edy Satriawan Engelbertus Mite Faisal Faisal Fathurrahman Fathurrahman Hammad, Rifqi Hariyadi, I Putu Hendra Husain Husain Husain Husain Husain I Gede Bagus Arthana I Made Yadi Dharma I Made Yadi Dharma I Made Yadi Dharma I Putu Hariyadi I Putu Hariyadi Ikrom, Paizul Kartarina Kartarina Khairan marzuki Khirullah, Zidan Kurniadin Abd Latif Kurniawan Kurniawan Lilik Widyawati Lilik Widyawati Mayadi Mayadi Mayadi Mayadi Mayadi, Mayadi Melati Rosanensi Mia Mubiatma Muhamad Azwar Muhamad Azwar Muhamad Azwar Muhamad Azwar, Muhamad Muhammad Azwar Muhammad Munawir Gazali Muliadi Muliadi Ni GustiAyu Dastriani Ondi Asroni Qulub, Mudawil Rana, Dwi Sadam Husen Santoso, Heroe Sirojul Hadi Sukma, I Gusti Ayu Yunita Suriyati ., Suriyati Suriyati Suriyati Suryati Suryati Syahrir, Moch. Tanwir Tanwir Triwijoyo, Bambang Krismono Yayan Suganda Zulfikri, Muhammad Zulkipli Zulkipli Zulkipli Zulkipli