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All Journal Majalah Ilmiah Teknologi Elektro 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 JOURNAL OF APPLIED INFORMATICS AND COMPUTING 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 Majalah Ilmiah Teknologi Elektro 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 Energy Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Mechanical Engineering Social Sciences Other

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Expert System for Identifying Pregnant Using Forward Chaining Gilang Aditia; Afzal Ziqri; Aldhan Tri Maulana; Faisal Dharma Adhinata
Journal of INISTA Vol 5 No 2 (2023): May 2023
Publisher : LPPM INSTITUT TEKNOLOGI TELKOM PURWOKERTO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20895/inista.v5i2.494

Abstract

Pregnancy is a biological process in which sperm and eggs meet each other to fertilize, and the fetus is formed in the uterus. But it's difficult; pregnant mothers sometimes have problems or discomfort during pregnancy. In addition, in areas far from the city, there are many obstacles to consulting an obstetrician. Therefore, it will be dangerous if mothers experience problems and find it difficult to get first aid. This research aims to create an expert system for pregnant women where it is not difficult for a mother to go to the doctor to ask about her complaints. The solution offered in this study is easy to access to the SP BUMIL website and automatically enters all mothers' complaints into the system. This system also provides a diagnosis and advice to pregnant women as to the best steps and an explanation of what the pregnant woman is suffering from. This expert system uses the forward chaining method, which has the advantage of producing a solution to a problem; in other words, being able to consider a problem and draw conclusions according to the facts. On this website, there is a disease information menu and also the results of the diagnosis
Sentiment Analysis on Tiktok Application Reviews Using Natural Language Processing Approach Abdul Majid; Dian Nugraha; Faisal Dharma Adhinata
Journal of Embedded Systems, Security and Intelligent Systems Vol. 4 No. 1 (2023): Vol 4, No 1 (2023): May 2023
Publisher : Program Studi Teknik Komputer

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

Abstract

Technology today is very developed, there are so many media that can be used to communicate, these media are very easy to use by connecting to the internet network. Research on the sentiment of this analysis can still be relatively small and new. The rapid development of technology today makes it very easy for humans to communicate with one of the modern technologies, namely smartphones. The initial stage of this research begins with the review to be analyzed, then continues with the collection of review data. Conducted on reviews that have been collected with and without an NLP approach resulting in 2 datasets, with an NLP approach and datasets without an NLP approach. The first step is to identify the problem with the research object. It then looked for related literature studies from both journals and review proceedings used as many as 1000 reviews, which have been labeled by 5 correspondents and resulted in positive reviews and negative reviews. The review is used as a dataset, then pre-processed with an NLP approach. Classification using the NLP approach got an accuracy of 76.92%, a precision of 80.00% and a recall of 74.07%, while without NLP it only got an accuracy of 69.23%, a precision of 80.00% and a recall of 64.52% At the preprocessing stage, the stemming feature, and stopword removal features were applied to each review. Word normalizer to handle variations in writing words that have the same meaning to be counted as a single term Furthermore, a stopword removal process is carried out to remove the stopword from the review.
A WEB-BASED INFORMATION SYSTEM FOR LECTURER'S PERFORMANCE APPRAISAL USING RATING SCALE METHODS Diovianto Putra Rakhmadani; Faisal Dharma Adhinata
Jurnal Riset Informatika Vol. 3 No. 2 (2021): March 2021 Edition
Publisher : Kresnamedia Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34288/jri.v3i2.65

Abstract

Online learning is widely used by every educational institution during the Covid-19 pandemic. Without face-to-face meetings, lecturers are required to present quality learning with feedback from students. The problem that arises is that EDOM is considered too long in terms of data processing, while lecturers are required to carry out quality teaching at each meeting. If students lose interest in a lecture due to the performance of the lecturer who is unable to make each virtual class attractive, the lecture activity will be ineffective. With the existence of a performance measurement system with the application of gamification that can measure the performance of lecturers at each meeting, lecturers can receive feedback while pursuing rewards or ratings on their performance. This study uses the waterfall model and produces a web-based information system that can be used as evaluation material in improving the quality of online learning.
A Comparative Analysis of Transfer Learning Architecture Performance on Convolutional Neural Network Models with Diverse Datasets Putra, Muhammad Daffa Arviano; Winanto, Tawang Sahro; Hendrowati, Retno; Primajaya, Aji; Adhinata, Faisal Dharma
Komputika : Jurnal Sistem Komputer Vol 12 No 1 (2023): Komputika: Jurnal Sistem Komputer
Publisher : Computer Engineering Departement, Universitas Komputer Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34010/komputika.v12i1.8626

Abstract

Deep learning is a branch of machine learning with many highly successful applications. One application of deep learning is image classification using the Convolutional Neural Network (CNN) algorithm. Large image data is required to classify images with CNN to obtain satisfactory training results. However, this can be overcome with transfer learning architectural models, even with small image data. With transfer learning, the success rate of a model is likely to be higher. Since there are many transfer learning architecture models, it is necessary to compare each model's performance results to find the best-performing architecture. In this study, we conducted three experiments on different datasets to train models with various transfer learning architectures. We then performed a comprehensive comparative analysis for each experiment. The result is that the DenseNet-121 architecture is the best transfer learning architecture model for various datasets.
Expert System to Diagnose Diseases in Durian Plants using Naïve Bayes Nugraha, Narantyo Maulana Adhi; Rahardian, Reva; Kridabayu, Adam Nur; Adhinata, Faisal Dharma; Ramadhan, Nur Ghaniaviyanto
Building of Informatics, Technology and Science (BITS) Vol 3 No 3 (2021): December 2021
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (599.891 KB) | DOI: 10.47065/bits.v3i3.1077

Abstract

Durian is a fruit that is very popular and very easy to find throughout Indonesia. Durian fruit is a thorny fruit with a very pungent smell with a distinctive taste, and for some durian fans, the distinctive taste of durian is what makes durian unique compared to other fruits. However, it is unfortunate that the production and quality of durian fruit in Indonesia is currently still low due to the limited knowledge of farmers in caring for and maintaining durian plants from pests and diseases on durian plants. So far, in detecting pests and diseases, farmers still carry out pest and disease detection manually, and of course, this is very dependent on pest and disease observers/experts. For this reason, so that later the level of production and quality of durian in Indonesia can increase, we create an expert system to diagnose a disease in durian plants to help farmers overcome problems around pests and diseases commonly occur in durian plants. This study uses the Naïve Bayes method as a determinant of durian disease. The experimental results yield an accuracy of 82%, which indicates the proposed method is quite good in diagnosing durian disease.
YOLO Algorithm for Detecting People in Social Distancing System Adhinata, Faisal Dharma; Rakhmadani, Diovianto Putra; Segara, Alon Jala Tirta
Jurnal Transformatika Vol. 19 No. 1 (2021): July 2021
Publisher : Jurusan Teknologi Informasi Universitas Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26623/transformatika.v19i1.3582

Abstract

Social distancing is an effort to prevent the spread of the coronavirus. Several systems for monitoring social distancing have been developed. People detection is an essential step in implementing a social distancing system. Failure to detect people causes the social distancing system to be inaccurate. Two people who communicate cannot occur violations of social distancing because one person is not detected. Therefore, we propose a precise person detection method for the social distancing system. The proposed social distancing system uses the YOLOv3 method for people detection and Euclidean Distance for measuring the distance of social distancing. YOLOv3 can detect people's objects precisely, even people who are caught small by the camera. Experiments on two outdoor video datasets result in an F1 value of more than 0.8. This proposed system can serve as a reference for future social distancing research.
Classification Taxonomies Genus of 90 Animals Using Transfer Learning Resnet-152 Saputro, Satria Nur; Adhinata, Faisal Dharma; Athiyah, Ummi
CommIT (Communication and Information Technology) Journal Vol. 18 No. 1 (2024): CommIT Journal
Publisher : Bina Nusantara University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21512/commit.v18i1.9482

Abstract

The process of learning theory and the limited ability to remember anything, especially a foreign language, often cause students to have difficulty understanding lessons, especially in determining the type and taxonomy of the animal. With the assistance of computer vision technology, students can more effectively face various challenges, enhance their understanding, and improve their ability to apply the concept of animal classification. The research classifies the taxonomy of 90 animals using Transfer Learning ResNet 152. It aims to analyze the performance of Transfer Learning ResNet 152 on the 90-animal dataset. The results show that in Model A with an architecture with frozen layers in 6 ResNet blocks, the highest evaluation value obtained is 0.9222 on Batch size 4 with Dropout 6, 0.9241 on Batch size 8 with Dropout 7, 0.9259 on Batch size 16 with Dropout 8, and 0.9296 on Batch size 32 with Dropout 4 and Dropout 7. Meanwhile, in model B with an architecture with frozen layers in 5 ResNet blocks and one non-frozen block, the highest evaluation value obtained is 0.7611 on Batch size 4 with Dropout 8, 0.8713 on Batch size 8 with Dropout 2, 0.8852 on Batch size 16 with Dropout 1, and 0.9204 on Batch size 32 with Dropout 3.
Sistem Kendali Proporsional pada Robot Penghindar Halangan (Avoider) Pioneer P3-DX Akhmad Jayadi; Try Susanto; Faisal Dharma Adhinata
Jurnal Teknologi Elektro Vol 20 No 1 (2021): (Januari - Juni ) Majalah Ilmiah Teknologi Elektro
Publisher : Program Studi Magister Teknik Elektro Universitas Udayana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/MITE.2021.v20i01.P05

Abstract

The basic ability that a mobile robot must have is to avoid obstacles, by being able to avoid obstacles, the robot will be able to do its job well without having to hit any obstacles, because by hitting an obstacle it will make the robot take longer to complete the mission even the robot can experience disorientation With the implementation of a control system on obstacle avoidance robots, the robot can overcome existing obstacles. Proportional control is a simple and easy to use control on a mobile robot, with eight sensors on the robot making the robot more sensitive to obstacles in front of it, so the pioneer type mobile robot P3-DX was used in this study. The robot has been able to pass through the existing obstacles with a Kp value of 2 and a constant speed of 4 without hitting it.
Nudibranch Suborders Classification based on Densely Connected Convolutional Networks Christyan, Timothy; Utama, Safitri Yuliana; Darmawan, Bagus Tri Yulianto; Adhinata, Faisal Dharma
JITCE (Journal of Information Technology and Computer Engineering) Vol. 8 No. 1 (2024)
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.8.1.30-37.2024

Abstract

Nudibranchs, often called sea slugs, are a group of soft-bodied marine gastropod mollusks that shed their shells after their larval stage. With their body structure that is very similar between one suborder and another, sometimes it is hard to tell apart the suborder of a nudibranch. In this work, we make an Image Classification model for determining the suborder of a nudibranch using deep learning algorithms DenseNet and EfficientNet. The experiment is conducted using Google Colaboratory environment. For DenseNet, we use 121, 169, and 201 layers; meanwhile, we only use the baseline algorithm for EfficientNet. The dataset for research is randomly taken from marine fauna forums on the internet. DenseNet with 201 layers shows a better generalization than other classifiers (accuracy of DenseNet 121, 169, 201, and baseline EfficientNet, respectively 53%, 41%, 73%, and 47%). The research produces a decent system for classifying the suborder of the Nudibranch. Usage of image recognition or background blurring systems in future research can improve the system's accuracy.
Klasifikasi Sampah Organik dan Non-Organik Menggunakan Convolutional Neural Network Abdurrahman Ibnul Rasidi; Yolanda Al Hidayah Pasaribu; Afzal Ziqri; Faisal Dharma Adhinata
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 1 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i1.4314

Abstract

Garbage is a unique problem in Indonesia. From ordinary waste to limited emergency plastic waste, Indonesia is the second-largest source of plastic waste in the world. Separate collection and disposal of waste is one way to reduce the waste generated by society and industry in Indonesia. Sorting out the types of waste is the first step before the recycling process. In the field of Computer Vision research, it is difficult to see the type and form of waste with a camera, therefore this study aims to overcome this problem by using Deep Learning technology which is expected to be implemented in the whole of Indonesia starting from some of the largest waste-producing cities. Deep Learning is a computer (AI) technique for learning like a human - with experiments being a Part of Machine Learning that can be used to classify images. The method used in this study uses the Convolutional Neural Network (CNN) method which can be used to detect and recognize objects in an image, which can be used to create an automatic waste classification system. Broadly speaking, CNN utilizes the convolution process by moving a convolution kernel (filter) of a certain size to an image, the computer gets new representative information from the results of multiplying that part of the image with the filter used. The test results show that the CNN method can classify inorganic waste with accuracy. 96% and organic waste with an accuracy of 62%.
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 Christoph Quix Christyan, Timothy Condro Kartiko Dani Azka Faz Darmawan, Bagus Tri Yulianto Dayal Gustopo Setiadjit 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 Rachma Wukir Purwitasari 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 Rival Fahmi Hidayat Rizki Rafiif Amaanullah Rohman Beny Riyanto Saputra, Rifqi Akmal Saputro, Satria Nur Satria Adi Nugraha Satrio Wibowo Sayyid Yakan Khomsi Pane Shalma, Hastin Ajeng Sofiyudin Pamungkas 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