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All Journal Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Jurnal Informatika Universitas Pamulang J-SAKTI (Jurnal Sains Komputer dan Informatika) JURTEKSI JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Jurnal ICT : Information Communication & Technology JSAI (Journal Scientific and Applied Informatics) Jurnal Informatika dan Rekayasa Elektronik JATI (Jurnal Mahasiswa Teknik Informatika) Respati G-Tech : Jurnal Teknologi Terapan JIKA (Jurnal Informatika) Jurnal Teknik Informatika (JUTIF) Jurnal Teknimedia: Teknologi Informasi dan Multimedia JNANALOKA Journal of Electrical Engineering and Computer (JEECOM) Information System Journal (INFOS) Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) International Journal of Artificial Intelligence and Robotics (IJAIR) Jurnal Pendidikan dan Teknologi Indonesia J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Informatika Teknologi dan Sains (Jinteks) Journal of Comprehensive Science Jurnal Ilmiah Sistem Informasi dan Ilmu Komputer Jurnal Bangkit Indonesia Jurnal Pendidikan Indonesia (Japendi) Prosiding SEMNAS INOTEK (Seminar Nasional Inovasi Teknologi) Intechno Journal : Information Technology Journal The Indonesian Journal of Computer Science International Journal of Information Engineering and Science
DHANI ARIATMANTO
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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 Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Mechanical Engineering Social Sciences Transportation Other

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Virtual Space For Virtual Reality Exhibitions With Oculus Quest Devices Rusdi Rahman, Muhammad; Suyanto, M; Ariatmanto, Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12952

Abstract

Based on information from the Ministry of Cooperatives and MSMEs, currently the number of MSMEs has reached 64.2 million euros and their share of GDP is 61.07% or 8,573.89 trillion rupiah (Coordinating Ministry for the Economy of the Republic of Indonesia, 2021). The contribution of MSMEs to the Indonesian economy includes the ability to absorb 97 percent of the current total employment and generate 60.4 percent of the total investment. (Ministry of Investment, 2021). VR (Virtual Reality) technology is a technology that allows users to feel in a virtual (virtual) world in visual form and users can interact with a virtual environment simulated by a computer in the form of Android. The focus of this research is a technical way of creating a virtual space or virtual space and 3D objects in displaying products from SMEs to be marketed to consumers with the virtual reality method using the oculus quest device, this research uses the Luther method, a six-stage process for creating multimedia that includes concept, design, material gathering, assembly, testing, and distribution. System testing was carried out using black box testing and usability testing using the SUS Score standardization, with a total of 47 respondents getting an average score of 54%. This average number exceeds 50% of the standard SUS Score Analysis, so the virtual reality exhibition space is categorized as suitable and OK for use by users. And it can also help MSMEs in carrying out virtual reality-based online marketing.
Detection And Classification of Citrus Diseases Based on A Combination of Features Using the Densenet-169 Model Firdaus, M. Haikal; Utami, Ema; Ariatmanto, Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 7 No. 4 (2023): Article Research Volume 7 Issue 4, October 2023
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i4.12974

Abstract

This research is motivated by the urgent need to improve the capability of detecting diseases in citrus plants, which play a crucial role in maintaining agricultural sector productivity. Diseases such as blackspot, canker, and greening can have a serious impact on harvest yields and overall plant health. Therefore, this research aims to enhance the accuracy in classifying diseases in citrus plants by applying a Deep Learning approach. In this study, we chose to adopt the DenseNet-169 architecture and conducted experiments with two different scenarios: one using original features and the other using a combination of features. This method was employed to classify four different classes, namely blackspot, canker, greening, and healthy plants, using an LDI dataset consisting of 3,000 images. This dataset was divided into three parts, namely training, testing, and validation sets. The experimental results indicate that the DenseNet-169 model with the use of feature combination achieved the highest accuracy rate at 96.66%, whereas the model using only original features achieved 91.33%. This significant improvement of 5.33% in accuracy provides strong evidence that the feature combination approach has a highly meaningful positive impact on the model's ability to identify and classify diseases in citrus plants. These findings confirm that the use of feature combinations is a highly effective strategy in improving the model's performance in disease classification tasks in citrus plants.
Methods for Development Mobile Stunting Application: A Systematic Literature Review Karim, Hildamayanti; Dhani Ariatmanto
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 1 (2024): Articles Research Volume 8 Issue 1, January 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.13123

Abstract

Stunting is a growth disorder in children. Stunting is one of the indicators of failure to thrive in toddlers caused by a chronic lack of nutritional intake in the first 1,000 days of life, from a fetus to a child aged 23 months. Based on data from the Asian Development Bank (ADB), in 2022 the percentage of stunting prevalence occurring in children aged <5 years in Indonesia reached 31.8%. So Indonesia is ranked 10th in Southeast Asia. The object of this review is to review the current literature and help researchers to find out what methods have been used in making stunting prevention applications. In a systematic search of the literature using quality databases including SpringerLink, ScienceDirect, and IEEE Xplore. The paper included in this review is a stunting prevention application information system by describing the methods most often used by researchers in making the stunting prevention application information system. There were 41 results based on the exclusion of titles and abstracts, based on the introduction and exclusion of conclusions there were 35 results, so we included 12 results for the full-text exclusion in the final analysis. So that the popular method used by researchers in Android-based stunting applications is the prototype method. Compared to other methods, prototyping is more suitable for systems that are made based on user needs.
Classification of types Roasted Coffee Beans using Convolutional Neural Network Method Metha, Halifa Sekar; Kusrini, Kusrini; Ariatmanto, Dhani
Sinkron : jurnal dan penelitian teknik informatika Vol. 8 No. 2 (2024): Article Research Volume 8 Issue 2, April 2024
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v8i2.13517

Abstract

In the current digital era, the role of technology in the agricultural industry is very necessary to increase yields which can have an impact on the productivity and welfare of farmers. Coffee is a drink that has been very popular for many years. Due to the high demand for coffee beans, this research aims to develop a system that can classify types of roasted coffee beans based on images using the Convolution Neural Network (CNN) method. Coffee bean processing is the most important stage in the coffee industry, classifying coffee beans often requires more in-depth knowledge and extensive experience regarding coffee beans. Therefore, this system can be a more effective solution. The author collects a dataset containing types of roasted coffee beans, then the Convolutional Neural Network (CNN) can analyze in the form of visual patterns each type of coffee bean. This implementation is expected to help the coffee industry identify coffee beans quickly and accurately.
STUDI PERBANDINGAN KECEPATAN, UKURAN, KUALITAS VRAY DAN ARNOLD DALAM PROSES RENDERING 3D MODEL ARSITEKTURAL Nugraha, Bhanu Sri; Nurrizky, Akbar; Ariatmanto, Dhani; Lukman, Lukman
Information System Journal Vol. 6 No. 02 (2023): Information System Journal (INFOS)
Publisher : Universitas Amikom Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24076/infosjournal.2023v6i02.1333

Abstract

Vray dan Arnold merupakan render engine yang dapat digunakan menggunakan software autodesk maya. Untuk mengetahui karakter kedua render engine tersebut peneliti melakukan observasi dan studi literatur yang kemudian mencari sampel data berupa 3D modelling. Pengumpulan data rendering dibagi menjadi tiga yaitu data kecepatan rendering, data ukuran rendering, dan data tampilan hasil rendering. Pengumpulan data kecepatan dan ukuran menggunakan metode pendekatan kuantitatif yang kemudian dilakukan pengujian, sedangkan data tampilan menggunakan kuesioner yang menggunakan metode pendekatan kualitatif yang tanpa menggunakan populasi dan sampel kemudian dilakukan pengujian. Hasil dari penelitian ini menunjukkan bahwa render engine vray dan Arnold memiliki perbedaan yang signifikan berdasarkan dari kecepatan dan ukuran rendering. Arnold memiliki waktu rendering dan ukuran file yang dihasilkan lebih unggul dibandingkan vray dengan rata-rata nilai kecepatan 0,0580 menit dan rata-rata nilai ukuran 0,63800 MB, sedangkan dari segi tampilan vray memiliki keunggulan dikarenakan vray menghasilkan gambar yang bagus dan tampak realistis dengan nilai rata-rata 3,09 responden menjawab “Sangat Bagus” dan 3,05 responden menjawab “Sangat Realistis”.
Analisa Kemampuan Algoritma YOLOv8 Dalam Deteksi Objek Manusia Dengan Metode Modifikasi Arsitektur Setiyadi, Aris; Utami, Ema; Ariatmanto, Dhani
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.694

Abstract

Object detection is a skill that can be taught to a machine with the help of a camera sensor to capture a digital image. By using the YOLO algorithm we can teach machines to detect, for example, humans. Much research in object detection has been carried out previously using different algorithms and methods and also on different objects and images. In this research, a method was carried out to modify the architecture of YOLOv8 in the head section to be used to detect human objects in grayscale images. The training process was carried out 4 times using the default architecture, Model 1, 2 and 3 architecture. With the default model results, the mAP value was 76, Model 1 had an mAP value of 66, model 2 had an mAP value of 81 and model 3 produced an mAP value of 80. From the research carried out modifications The YOLOv8 architecture in the head section can influence the training results and produce a better model than the default architecture which only produces an mAP value of 76. The best results were obtained in model 2 with layers used of 40x40x512xW resulting in a model with an mAP value of up to 81.
OPTIMALISASI PROSES PEMBAHARUAN LOCATOR PADA KODE OTOMATIS SELENIUM DENGAN MENGGUNAKAN PAGE OBJECT MODEL Ramdhani, Mohamad Dhicy; Setyanto, Arief; Ariatmanto, Dhani
Jurnal Informatika Vol 8, No 1 (2024): JIKA (Jurnal Informatika)
Publisher : University of Muhammadiyah Tangerang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31000/jika.v8i1.9710

Abstract

Pengujian aplikasi adalah proses penting pada pengembangan perangkat lunak. Salah satu cara mempercepat pengujian aplikasi dengan menggunakan teknik pengujian otomatis menggunakan selenium webdriver. Teknik ini menggunakan kode otomatis yang disusun di dalam sebuat test case. Pada test case terdapat langkah pengujian yang terdiri dari banyak locator. Locator merupakan query untuk mengakses element pada website yang merepresentasikan objek yang diakses oleh pengguna seperti tombol, input teks dan lainnya. Locator yang sama bisa digunakan pada test case yang berbeda Pada penelitian sebelumnya, automatisasi pengujian perangkat lunak berbasis website terbukti meningkatkan waktu uji yakni 30 detik lebih cepat dibandingkan proses pengujian secara manual. Namun pada penelitian tersebut tidak dibahas bagaimana proses pembaharuan locator yang efektif ketika terjadi perubahan struktur sistem aplikasi. Penelitian ini bertujuan untuk mengoptimalisasi proses pembaharuan kode automatisasi dengan mengolah data locator menggunakan teknik page object modelling (POM) pada halaman login website. Setiap locator ditempatkan pada satu tempat yang disebut dengan repositori sehingga proses pembaharuan locator dilakukan secara sentralisasi pada repositori tanpa harus mencari dan membuka test case satu per satu.Hasil penelitian menunjukkan efisiensi sebesar 14.28 % lebih cepat jika dibandingkan dengan proses pembaharuan tanpa metode POM
Sentiment Analysis to Measure Public Trust in the Government Due to the Increase in Fuel Prices Using Naive Bayes and Support Vector Machine Zakaria, Zakaria; Kusrini, Kusrini; Ariatmanto, Dhani
International Journal of Artificial Intelligence & Robotics (IJAIR) Vol. 5 No. 2 (2023): November 2023
Publisher : Informatics Department-Universitas Dr. Soetomo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25139/ijair.v5i2.7167

Abstract

The study examines public sentiment on the government's fuel price policy using an experimental approach and Twitter data obtained through API scraping. It applies sentiment analysis methods like Naïve Bayes, SVM, and Majority Voting. SVM achieved 85% accuracy, excelling in identifying negative sentiments, while Majority Voting reached 70% by considering confidence levels. Naïve Bayes struggled with neutral sentiments. They are combining methods to enhance the understanding of public sentiments on fuel price changes. The study highlights sentiment analysis' effectiveness in gauging reactions to fuel policies, with SVM offering more profound insights into sentiments related to fuel price hikes. Challenges remain in identifying neutral sentiments due to social media text brevity. These findings underscore the contextual importance of interpreting sentiment analysis. Leveraging these insights, governments can understand public perceptions better and devise improved communication strategies for sensitive economic policies like fuel price hikes, fostering better government-citizen interactions. The study aims to guide stakeholders in comprehending public perspectives within public policy, emphasizing the relevance of sentiment analysis for policy evaluation.
Analisa Kemampuan Algoritma YOLOv8 Dalam Deteksi Objek Manusia Dengan Metode Modifikasi Arsitektur Setiyadi, Aris; Utami, Ema; Ariatmanto, Dhani
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.694

Abstract

Object detection is a skill that can be taught to a machine with the help of a camera sensor to capture a digital image. By using the YOLO algorithm we can teach machines to detect, for example, humans. Much research in object detection has been carried out previously using different algorithms and methods and also on different objects and images. In this research, a method was carried out to modify the architecture of YOLOv8 in the head section to be used to detect human objects in grayscale images. The training process was carried out 4 times using the default architecture, Model 1, 2 and 3 architecture. With the default model results, the mAP value was 76, Model 1 had an mAP value of 66, model 2 had an mAP value of 81 and model 3 produced an mAP value of 80. From the research carried out modifications The YOLOv8 architecture in the head section can influence the training results and produce a better model than the default architecture which only produces an mAP value of 76. The best results were obtained in model 2 with layers used of 40x40x512xW resulting in a model with an mAP value of up to 81.
Analisis Perbandingan Algoritma Machine Learning Dan Deep Learning Untuk Sentimen Analisis Teks Umpan Balik Tentang Evaluasi Pengajaran Dosen Setiawan, Hadiguna; Ariatmanto, Dhani
JSAI (Journal Scientific and Applied Informatics) Vol 7 No 2 (2024): Juni
Publisher : Fakultas Teknik Universitas Muhammadiyah Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36085/jsai.v7i2.6572

Abstract

Evaluation of lecturer performance is very important because it helps in monitoring and ensuring that lecturers fulfill their duties effectively in maintaining integrity and teaching lecture material. By assessing lecturer performance based on criteria such as teaching, it can identify areas for improvement and provide support if needed. This study aims to determine the accuracy level of machine learning and deep learning combined with word-embedding for text analysis of lecturer teaching performance evaluation using preprocess techniques.The dataset consisted of 663 positive data, 552 negative data, and 465 neutral data. Successful in the results of the experiment, the training accuracy value for each classification method included KNN of 74.75%, SVM of 65.78%, RF of 98.58%, LSTM of 95.64% and Bi-LSTM of 95.91%. The test accuracy value for each classification method includes KNN of 59.82%, SVM of 62.88%, RF of 69.37%, LSTM of 70.81% and Bi-LSTM of 72.25%. The most superior method in processing data of 663 positive data, 552 negative data, and 465 neutral data by applying the word-embedding method, namely BiLSTM with a training accuracy of 95.91% and a testing accuracy of 72.25%.
Co-Authors Adrianto M. Wijaya Ahsan, Muhammad Salimy Alfansani, Abdul Rauf Alva Hendi Muhammad Amali, Amali Anggriandi, Dendi Anjar Setiawan Anton, Tri Arfian Hendro Priyono Arfian Yogi Ferianto Arief Setyanto Aris Setiyadi Bangun Watono BHANU SRI NUGRAHA Chaedar Fatach, Muhamad Reza Darmawan, Eko Rahmad Didi Kurniawan Edy Widodo Ema Utami Eva Putriany Fadilla, Rizqi Mirza Fahma Inti Ilmawati Firdaus, M. Haikal Ghifari, Dloifur Rohman Al Hadiguna Setiawan Hayaty, Mardhiya Jordy, Roy Karim, Hildamayanti Khairani, Fatihatul KUSRINI Kusrini Kusrini Kusrini, Kusrini Linggama, Muhamad Nurfaizi Lukman Lukman M. Ilham Arief M. Ulil Albab Majid, Muhammad Arif Kholis Metha, Halifa Sekar Mohammad Suyanto Muhammad Nabil Muhammad Rifa'i, Anggi Muhammad Salimy Ahsan Mulia Sulistiyono Norhikmah . Nugroho, Agung Nurrizky, Akbar Nur’aini Pramudyantoro, Arvi Prasetiyanto, Ari Eka Prayoga, Dimas Putriany, Eva Quratul Ain Ramdhani, Mohamad Dhicy Rifai, Anggi Muhammad Rosmantyo, Windhy Rokhmat Rudiyan, Ari Rusdi Rahman, Muhammad Selvi Marcellia Silvi Agustanti Bambang Slameto, Andika Agus Subandono, Anelta Tirta Putri Sukoco Sukoco Supriadi, Oki Akbar Suryono, Wachid Daga Suyanto, M Tikasni, Elisa Triesa Wea, Engel Bertus Turmudi Zy, Ahmad Wijaya, Adrianto Mahendra Wiwik Handayani Zakaria Zakaria zakinah, annisa gatri Zuhri, Muhammad Rafli Zulpan Hadi