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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) CommIT (Communication & Information Technology) Jurnal Transformatika JUITA : Jurnal Informatika Journal of Information Systems Engineering and Business Intelligence Indonesian Journal on Computing (Indo-JC) Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Knowledge Engineering and Data Science Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA JITK (Jurnal Ilmu Pengetahuan dan Komputer) JOURNAL OF APPLIED INFORMATICS AND COMPUTING Jurnal Pertahanan : Media Informasi tentang Kajian dan Strategi Pertahanan yang Mengedepankan Identity, Nasionalism dan Integrity DoubleClick : Journal of Computer and Information Technology Journal of Information Technology and Computer Engineering JURIKOM (Jurnal Riset Komputer) Logista: Jurnal Ilmiah Pengabdian Kepada Masyarakat KOMPUTIKA - Jurnal Sistem Komputer Jurnal Riset Informatika Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar Building of Informatics, Technology and Science Jurnal Teknologi Informasi dan Multimedia RADIAL: JuRnal PerADaban SaIns RekAyasan dan TeknoLogi Jurnal Teknik Elektro dan Komputasi (ELKOM) Jurnal E-Komtek Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer System and Informatics (JoSYC) Madani : Indonesian Journal of Civil Society Journal of Informatics, Information System, Software Engineering and Applications (INISTA) Jurnal Teknik Informatika (JUTIF) Journal of Informatics and Vocational Education Teknika ICTEE (Engineering Journals of Information, control, telecommunication and electrical) Insyst : Journal of Intelligent System and Computation Journal of Dinda : Data Science, Information Technology, and Data Analytics IJCOSIN : Indonesian Journal of Community Service and Innovation Journal of Embedded Systems, Security and Intelligent Systems El-Mujtama: Jurnal Pengabdian Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) RADIAL: Jurnal Peradaban Sains, Rekayasa dan Teknologi Jurnal Komtika (Komputasi dan Informatika) Jurnal Kajian Ilmu dan Teknologi (JKIT)
Faisal Dharma Adhinata
Institut Teknologi Telkom Purwokerto
Religion Agriculture, Biological Sciences & Forestry 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 Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Model Deteksi Kebakaran Hutan dan Lahan Menggunakan Transfer Learning DenseNet201 Saputra, Rifqi Akmal; Adhinata, Faisal Dharma
Intelligent System and Computation Vol 5 No 2 (2023): INSYST: Journal of Intelligent System and Computation
Publisher : Institut Sains dan Teknologi Terpadu Surabaya (d/h Sekolah Tinggi Teknik Surabaya)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52985/insyst.v5i2.317

Abstract

Kebakaran hutan dan lahan di Indonesia merupakan peristiwa yang sering terjadi dan menimbulkan kerugian yang signifikan dalam bidang kesehatan, ekologi, dan sosial. Faktor manusia dan alam berperan dalam memicu terjadinya kebakaran ini. Namun, penanganan kebakaran hutan dan lahan masih menghadapi kendala dalam memprediksi lokasi titik panas secara akurat, sehingga pengendalian yang optimal sulit dilakukan. Oleh karena itu, diperlukan pengembangan sistem cerdas untuk mendeteksi kebakaran hutan dan lahan dengan lebih efektif. Penelitian ini bertujuan untuk menciptakan sebuah model yang mampu mendeteksi kebakaran hutan dan lahan dengan menggunakan pendekatan transfer learning, dengan memanfaatkan arsitektur DenseNet201 guna meningkatkan akurasi deteksi. Dataset yang digunakan dalam penelitian ini berasal dari Fire Forest Dataset pada situs Kaggle. Proses ekstraksi fitur dilakukan menggunakan arsitektur DenseNet201, dan model yang dihasilkan diuji dengan menggunakan metode confusion matrix untuk mengklasifikasikan gambar menjadi dua kelas, yaitu kelas api dan non-api. Melalui pelatihan menggunakan arsitektur DenseNet201, diperoleh model yang efektif dalam mendeteksi kebakaran hutan dan lahan. Hasil pengujian dengan menggunakan data uji sebanyak 380 data menunjukkan tingkat akurasi sebesar 99% dalam mengenali gambar kebakaran hutan dan lahan. Penelitian ini memberikan kontribusi penting dalam pengembangan teknologi deteksi kebakaran hutan dan lahan. Penggunaan pendekatan transfer learning dengan arsitektur DenseNet201 memiliki potensi untuk meningkatkan akurasi deteksi kebakaran yang lebih baik. Diharapkan penelitian ini dapat memberikan landasan bagi pengembangan sistem cerdas yang lebih canggih dan efektif dalam mengatasi masalah kebakaran hutan dan lahan, serta melindungi lingkungan dan kesehatan masyarakat di Indonesia.
Web-based Information System for Processing Student Report Grade Using Waterfall Method (Study Case: SMPN 3 Talaga) Lisan, Fauzan Fashihul; Riadi, Daffa Rayhan; Nugraha, Aditya Rizkiawan; Shalma, Hastin Ajeng; Adhinata, Faisal Dharma
Jurnal CoreIT: Jurnal Hasil Penelitian Ilmu Komputer dan Teknologi Informasi Vol 9, No 2 (2023): December 2023
Publisher : Fakultas Sains dan Teknologi, Universitas Islam Negeri Sultan Syarif Kasim Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24014/coreit.v9i2.21954

Abstract

Assessment is an activity or method used by educators to measure students' abilities in the processes and learning outcomes at school. Junior Highschool 3 Talaga in its assessment process still uses the conventional method which causes delays in the assessment report process, not minimizing errors in writing on the assessment report is quite difficult. Based on the problems experienced, it is presented as an assessment information system that helps the student assessment process. This information system was created using the waterfall method which produces a ready-to-use system with sufficient features. The information system presented uses the PHP and MySQL programming languages to facilitate and lighten student assessment work. This assessment is used as a reference standard for achieving student competency and a basis for helping students. Not only that, but the assessment is also carried out continuously and aims to monitor the learning process and progress of students. With the existence of an information system, the assessment will be more efficient and can facilitate its implementation.
Sistem Pakar Diagnosa Penyakit pada Hewan Kucing Berbasis Web Ramadhan, Faiz Zaki; Aditya, Gilang; Nainggolan, Purnama Dileon Yamora; Adhinata, Faisal Dharma
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 2 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i2.5301

Abstract

Tahun 2018 Rakuten Insight menyurvei hewan jinak di Asia. Survei diikuti 97.000 responden dari benua asia timur dan asia tenggara. bahwa 59% orang memiliki binatang peliharaan. Dari banyaknya peliharaan, kucing menjadi pilihan, terutama diIndonesia. sebanyak 47% orang memelihara kucing. Kucing terkadang sering terkena penyakit, dan kita suka bingung apa yang terjadi dengan hewan kita, dan bagaimana cara kita bisa mengobatinya, terutama kota yang tidak memiliki rumah perawatan hewan. Untuk mengetahui penyakit apa yang diderita kucing, diperlukan informasi medis untuk mengetahui itu, sedangkan yang kita tahu masih sangat terbatas. akibatnya dibutuhkan sistem guna menyampaikan pengetahuan seperti seseorang pakar, penulis membuat sistem dimana berisi pengetahuan seseorang ahli penyakit pada kucing, agar masyarakat yang awam dapat mengetahui jenis penyakit serta penyembuhanya, rancangan sistem pakar menggunakan metode Naïve Bayes dimana pengklasifikasian probabilitasnya sederhana. keuntunganya naïve bayes hanya membutuhkan data kecil pelatihan untuk proses klasifikasi yang diperlukkan untuk parameter dalam membantu membuat sistem identifikasi penyakit. hasil contoh, kita menginputkan gejala-gejala seperti bulu rontok, lingkaran merah pada kulit, serta bercak putih seperti ketombe, dimana merupakan gejala pernyakit kadas seperti yang ada disystem. Hasil penelitian menunjukkan diagnosa penyakit kucing menggunakan naïve bayes dapat menghasilkan akurasi 93%.
Perancangan Sistem Informasi Berbasis Web Wiskul Banyumas Sebagai Sarana Informasi dan Promosi Pariwisata, Kuliner, serta Penginapan Di Banyumas Wibowo, Satrio; Agustyn, Zulfa Basmallah; Hidayat, Wahrul; Adhinata, Faisal Dharma
Jurnal Kajian Ilmu dan Teknologi (JKIT) Vol. 1 No. 1 (2024): Jurnal Kajian Ilmu dan Teknologi (JKIT)
Publisher : Rumah Jurnal PT Citra Air Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.71200/jkit.v1i1.4

Abstract

Berdasarkan penelitian yang telah dilakukan, diperoleh hasil bahwa suatu sistem informasi berbasis web sebagai sarana promosi dan informasi wisata, kuliner, serta penginapan di Banyumas belum tersedia. Berdasarkan penelitian tersebut penulis berinisiatif untuk membuat sebuah rancangan sistem informasi berbasis web menggunakan Laravel. Penulis menggunakan Metode Waterfall sebagai metode pengembangan. Dengan adanya sistem informasi berbasis web yang telah penulis rancang diharapkan dapat memudahkan wisatawan dalam bepergian dan membantu kabupaten Banyumas di sektor pariwisata. Rancangan sistem informasi berbasis web yang penulis buat menggunakan Bahasa Pemrograman PHP versi 7.4 framework Laravel, dengan menggunakan database mysql. Penulis berharap dengan adanya perancangan sistem informasi berbasis web ini membuat tempat wisata, kuliner, serta penginapan di Banyumas dapat lebih dikenal oleh masyarakat.
A Combination of Transfer Learning and Support Vector Machine for Robust Classification on Small Weed and Potato Datasets Adhinata, Faisal Dharma; Ramadhan, Nur Ghaniaviyanto; Fauzi, Muhammad Dzulfikar; Tanjung, Nia Annisa Ferani
JOIV : International Journal on Informatics Visualization Vol 7, No 2 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.2.1164

Abstract

Agriculture is the primary sector in Indonesia for meeting people's daily food demands. One of the agricultural commodities that replace rice is potatoes. Potato growth needs to be protected from weeds that compete for nutrients. Spraying using pesticides can cause environmental pollution, affecting cultivated plants. Currently, agricultural technology is being developed using an Artificial Intelligence (AI) approach to classifying crops. The classification process using AI depends on the number of datasets obtained. The number of datasets obtained in this research is not too large, so it requires a particular approach regarding the AI method used. This research aims to use a combination of feature extraction methods with local and deep feature approaches with supervised machine learning to classify of small datasets. The local feature method used in this research is Local Binary Pattern (LBP) and Histogram of Oriented Gradients (HOG), while the deep feature method used is MobileNet and MobileNetV2. The famous Support Vector Machine (SVM) uses the classification method to separate two data classes. The experimental results showed that the local feature HOG method was the fastest in the training process. However, the most accurate result was using the MobileNetV2 deep feature method with an accuracy of 98%. Deep features produced the best accuracy because the feature extraction process went through many neural network layers. This research can provide insight on how to analyze a small number of datasets by combining several strategies
Performance Evaluation of Face Mask Detection Using Feature Descriptor and Supervised Learning Method Suheryadi, Adi; Adhinata, Faisal Dharma; Wijanarko, Heru
Journal of Applied Informatics and Computing Vol. 10 No. 1 (2026): February 2026
Publisher : Politeknik Negeri Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30871/jaic.v10i1.11999

Abstract

The use of masks as a measure to prevent the spread of dangerous diseases such as COVID-19 and others has become a social norm. Manual detection is less effective, especially in areas with high mobility. This study develops and evaluates an artificial intelligence (AI)-based face mask detection system using feature description and machine learning models. An optimal and lightweight model can help hospitals implement face mask detection systems in areas prone to disease transmission. Image preprocessing, feature description, supervised learning model studies, and performance evaluation were conducted using accuracy, precision, recall, and F1-score metrics, and a confusion matrix was used to assess the overall model performance. The performance evaluation results show that the combination of the LBP feature description with the random forest model is the best choice, with a relatively high and stable accuracy of around 96.3% with an average value, precision, recall, and F1-score of around 96% using K-Fold Cross-Validation. These findings suggest that this method is helpful in detecting mask use while minimizing error and computation rates. This study contributes to the development of lightweight mask detection systems that can be used in real time.
Network Security Analysis Using PPTP and VPN on the Unmanned Aerial Vehicle PUNA MALE Simulation Prototype Zen, Bita Parga; Putro, Iwan Nofi Yono; Adhinata, Faisal Dharma
Jurnal Pertahanan: Media Informasi tentang Kajian dan Strategi Pertahanan yang Mengedepankan Identity, Nasionalism dan Integrity Vol 12, No 1 (2026)
Publisher : The Republic of Indonesia Defense University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33172/jp.v12i1.19915

Abstract

Reliable and secure communication is a fundamental requirement for Medium-Altitude Long-Endurance (MALE) Unmanned Aerial Vehicle (UAV) operations, where real-time data transmission must be maintained despite network instability. Internet-Protocol (IP)-based communication networks commonly used in UAV systems are susceptible to delay, jitter, and packet loss, which can degrade mission performance in surveillance and reconnaissance scenarios. This study investigates the capability of a Point-to-Point Tunneling Protocol (PPTP) Virtual Private Network (VPN) to simultaneously enhance communication security and preserve network Quality of Service (QoS) for PUNA MALE UAV communications. A Hardware-in-the-Loop (HIL) simulation environment implemented in GNS3 was developed to emulate realistic operational conditions. Network performance was evaluated using ICMP, TCP, and UDP protocols, with metrics including delay, jitter, and packet loss. Experimental results show that ICMP achieved an average jitter of 0.0879 ms with zero packet loss, while TCP demonstrated superior stability compared to UDP, which exhibited a packet loss rate of 1.9708%. The findings reveal that PPTP VPN deployment can maintain stable QoS performance while providing secure communication channels, indicating its feasibility as a lightweight security solution for UAV communication systems operating in controlled network environments. This work provides empirical evidence supporting the secure integration of VPNs for reliable MALE UAV data communication architectures.
ENHANCING ACCURACY OF WEATHER CLASSIFICATION USING DEEP FEATURES AND SUPPORT VECTOR MACHINE Raden Sumiharto; Faisal Dharma Adhinata
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 11 No. 4 (2026): JITK Issue May 2026
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v11i4.8251

Abstract

Weather is a determinant of farmers' planting calendar. Farmers usually start planting rice in the rainy season because rice requires sufficient water to produce optimal harvests. The weather is almost unpredictable in certain months, so farmers now look at cloud conditions to predict the season. Seasonal predictions based on cloud imagery can be assisted using Artificial Intelligence methods. Previous research used deep learning via transfer learning, but the results were not optimal. This research dataset is sourced from Kaggle and consists of five classes, namely cloudy, foggy, rainy, shine, and sunrise with a total data of 1500 images. This research proposes that a hybrid deep features and machine learning approach be used to increase the accuracy of the results. The MobileNet deep learning method is used at the feature extraction stage, then for classification using the Support Vector Machine (SVM) method. Experimental results with the Radial Basis Function (RBF) kernel on SVM produced an accuracy of 0.9500 for training data. The evaluation results using testing data produced an accuracy of 0.9667. This result also saw an increase of 4.2% in training data compared to previous research. Through these results, MobileNet-SVM is proven to be able to improve classification accuracy when using a small dataset with 1500 images.
Pengembangan Teknologi Rekomendasi Kecerdasan Buatan yang Digunakan pada Perpustakaan Devara, Emmanuel Genesius Evan; Rijanandi, Teguh; Riyanto, Rohman Beny; Adhinata, Faisal Dharma
Journal of Informatics and Vocational Education Vol 4, No 3 (2021): Journal of Informatics and Vocational Education
Publisher : Pendidikan Teknik Informatika dan Komputer, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/joive.v4i3.53618

Abstract

Perpustakaan merupakan tempat untuk membaca buku yang beragam koleksinya agar pembaca mendapatkan berbagai macam sumber ilmu. Namun di masa yang serba teknologi, manusia tentunya ingin hal yang lebih praktis. Dengan hadirnya Artificial intelligence maka dapat diterapkan dan diintegrasikan kedalam sistem perpustakaan. Permasalahan yang umum saat pembaca datang ke perpustakaan adalah mencari literatur yang sesuai pilihannya, baik dari segi nama, gambar, jenis, dan bentuk suatu literatur tersebut. Artificial intelligence dapat membantu dalam mencari literatur berdasarkan rekomendasi dan rating, sehingga pembaca tidak perlu repot-repot mencari literatur yang di inginkan satu per satu dari rak buku yang tersedia. Hal ini tentu memudahkan para pembaca dalam mencari literatur, terutama yang bingung harus mencari dari mana. Sistem rekomendasi yang digunakan adalah metode rekomendasi, dimana metode merupakan sebuah metode yang menggabungkan Filtering dan Ranking.  Penelitian ini ditujukan agar para pembaca yang berada di perpustakaan dapat dengan mudah dan cepat mencari literatur mereka.
Co-Authors Abdul Majid Abdurrahman Ibnul Rasidi Adam Nur Kridabayu Adil El-Faruqi Aditya Wijayanto Aditya, Gilang Afzal Ziqri Agustyn, Zulfa Basmallah Ahmad Muslih Syafi’i Ajeng Fitria Rahmawati Akhmad Jayadi Aldhan Tri Maulana Alfan Adi Chandra Alissyah Putri Alon Jala Tirta Segara Alya Aulia Hanafi Ananda Aulia Rizky Ananda Aulia Rizky Andra Aulia Rizaldy Anshari Rusmeniar R.A Apri Junaidi, Apri Arief Rais Bahtiar Arif Amrulloh Ariq Cahya Wardhana Bagus Bayu Sasongko Bita Parga Zen Christoph Quix Christyan, Timothy Condro Kartiko Dani Azka Faz Darmawan, Bagus Tri Yulianto Dayal Gustopo Setiadjit Devara, Emmanuel Genesius Evan Dian Nugraha Diovianto Putra Rakhmadani Emmanuel Genesius Evan Devara Fadlan Raka Satura Fajar Malik Falah Arfani Fauzi, Muhammad Dzulfikar Fawwaz Muhammad Zulfikar Febry Ardiansyah Firdonsyah, Arizona Fitran Dwi Pramakrisna Fitran Dwi Pramakrisna Gilang Aditia GITA FADILA FITRIANA Gracia Rizka Pasfica Hendrowati, Retno Herman Yuliansyah Hidayat, Wahrul Ibnul Rasidi, Abdurrahman Ikadhanny Yudyan Pratama Irsyad Zulfikar Jahfal Rizqi Putra Pradhana Kridabayu, Adam Nur Lisan, Fauzan Fashihul M Alfian Maulana Al Azhar Merlinda Wibowo Metha Khafifah Isty Rikhanah Mohammad Rifqi Zein Muhammad Arif Saputra Muhammad Fajar Ahadi Muhammad Ikhsan Muhammad Iqbal Rasyid Muhammad Pajar Kharisma Putra Nainggolan, Purnama Dileon Yamora Narantyo Maulana Adhi Nugraha Naseh Hibban Nasution, Annio Indah Lestari Nia Annisa Ferani Tanjung Nike Prasetyo Nisrina Eka Salsabila Novi Rahmawati Novi Rahmawati Nugraha, Aditya Rizkiawan Nugraha, Narantyo Maulana Adhi Nur Ghaniaviyanto Ramadhan Nur Syahela Hussien Nursatio Nugroho Pasaribu, Yolanda Al Hidayah Purnama Dileon Yamora Nainggolan Putra, Muhammad Daffa Arviano Putro, Iwan Nofi Yono Rachma Wukir Purwitasari Raden Sumiharto Rahardian, Reva Rahmanda Trinova Putra Ramadhan, Faiz Zaki Renna Nur Injiyani Reva Rahardian Riadi, Daffa Rayhan Rifki Adhitama, Rifki Rifqi Akmal Saputra Rifqi Alfinnur Charisma Rijanandi, Teguh Rival Fahmi Hidayat Riyanto, Rohman Beny Rizki Rafiif Amaanullah Saputra, Rifqi Akmal Saputro, Satria Nur Satria Adi Nugraha Satrio Wibowo Sayyid Yakan Khomsi Pane Shalma, Hastin Ajeng Sofiyudin Pamungkas Suheryadi, Adi Teguh Rijanandi Teguh Rijanandi Teguh Rijanandi Tri Dimas Cipto Satrio Wibowo Try Susanto Ummi Athiyah Utama, Safitri Yuliana Utami, Annisaa Vincent Nathaniel Wahyono Wahyono Widi Widayat Wijayanto, Danur Winanto, Tawang Sahro Yaqutina Marjani Santosa Yohani Setiya Rafika Nur Yolanda Al Hidayah Pasaribu Yuni nur fari'ah Zanuar Rahmat Saputra Ziqri, Afzal