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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Edukasi dan Penelitian Informatika (JEPIN) JUITA : Jurnal Informatika POSITIF Jurnal Hukum Novelty Sistemasi: Jurnal Sistem Informasi JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang DoubleClick : Journal of Computer and Information Technology Jurnal Penelitian dan Pengabdian Kepada Masyarakat UNSIQ KOMPUTIKA - Jurnal Sistem Komputer Jurnal Repositor Jurasik (Jurnal Riset Sistem Informasi dan Teknik Informatika) Jurnal Informatika dan Teknologi Komputer ( J-ICOM) IICS JHCLS Jurnal Nasional Teknik Elektro dan Teknologi Informasi Makara Journal of Technology Jurnal Pengabdian Hukum Indonesia
Wicaksono, Galih Wasis
Universitas Muhammadiyah Malang
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 Law, Crime, Criminology & Criminal Justice Materials Science & Nanotechnology Mechanical Engineering Other

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Sistem Pendukung Keputusan Pemilihan Lokasi Tanah Strategis di Kota Mataram Menggunakan Metode AHP-TOPSIS Dati Nafa Alfiana; Christian Sri Kusuma Aditya; Galih Wasis Wicaksono
Jurnal Repositor Vol 5 No 1 (2023): Februari 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/repositor.v5i1.1473

Abstract

Land location selection is very important for investors, businessmen, the community or for newcomers. The strategic land selection decision support system in the city of Mataram using the AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) methods has the aim of providing recommendations for strategic land locations to build shops, shopping centers or boarding houses or rented houses. for investors, businessmen or newcomers. In this study, the AHP method used to determine the criteria was carried out by the AHP method while the ranking stage was carried out by the TOPSIS method. In this study using 4 criteria, namely price, location, area, risk and getting results in the first rank, namely the value of 0.792853 and the tests carried out in this study were testing the accuracy of the results with calculations using the Cross-validation and the accuracy values obtained from the combination of both methods reached 85%.
ResNet101 Model Performance Enhancement in Classifying Rice Diseases with Leaf Images Galih Wasis Wicaksono; Andreawan
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 7 No 2 (2023): April 2023
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v7i2.4575

Abstract

Indonesia is the fourth biggest rice producer in Asia with its production accounting for 35.4 million metric tons yearly. This figure can increase unless rice crop failure is resolved. Identifying rice diseases, however, may serve as an approach to minimizing the risk of crop failure. The classification to detect rice diseases was previously researched using ResNet101 method with 100% accuracy. Despite this perfect accuracy, this approach does not come without an issue, where the prediction is not yet optimal for each label and loss results which are regarded as too high due to overfitting. Departing from this issue, this research aims to improve the model by reducing the layer complexity of the model and comparing two layers structures of the model, two different data, and the ResNet101 model. The performance resulting from the model could be enhanced with the structuring of simple architectural layers. Despite the small quantity of dataset, the model performance can yield 100% accuracy in the classification of rice diseases with a loss value of 2.91%. The model performance in this research experienced a 2.7% increase at the loss value and it could accurately classify the type of rice diseases according to leaf images on each label. The problem solved by this research is that ResNet101 is able to classify rice disease accurately even with a small amount of data by utilizing the appropriate layer arrangement with data requirements. In addition, the overfitting that occurred in previous research can also be resolved properly. This matter proves that the correlation between the layers of the model with the amount of data is very influential.
Comparison of Classification Methods on Twitter Sentiment Analysis of PDAM Tugu Tirta Kota Malang Anisa Dewi Anggraeni; Muhammad Farhansyah; Muhammad Risky Pratama Hermawan; Galih Wasis Wicaksono; Christian Sri Kusuma Aditya
JUITA: Jurnal Informatika JUITA Vol. 11 No. 1, May 2023
Publisher : Department of Informatics Engineering, Universitas Muhammadiyah Purwokerto

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30595/juita.v11i1.15485

Abstract

The Regional Drinking Water Company (PDAM) Tugu Tirta is a public service company in Malang's drinking water distribution field. The company uses a customer complaint feature that is provided on the website. However, only a few people know about it and use it. From this problem, the researcher uses social media data, namely Twitter, to explore data sources and collect feedback tweets from the customer. However, analyzing the sentiment of the 1000 data used is elusive. The tweets contain unstructured text, so the researcher applies the labeling from the dataset, preprocesses the text, and then extracts the tweets by applying the classification methods by comparing Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), Logistic Regression (LR), Short-Term Long-Term Memory (LSTM), and Indonesian BERT to achieve highly accurate results. The tests with six methods show that Logistic Regression and Indonesian BERT are the best methods, with an accuracy of 85%. In this study, we obtained an effective algorithm to classify a comment as positive, negative, or neutral related to the Tugu Tirta Regional Drinking Water Company (PDAM).
Desain Perangkat Pembelajaran Pendidikan Tinggi dengan Sistem Lective Gegulang™ Galih Wasis Wicaksono; Hari Windu Asrini; Muhammad Andi Al-Rizki
Jurnal Nasional Teknik Elektro dan Teknologi Informasi Vol 6 No 4: November 2017
Publisher : Departemen Teknik Elektro dan Teknologi Informasi, Fakultas Teknik, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (992.732 KB)

Abstract

Syllabus and lesson plan (RPS and RPP in Indonesian), which consist of topics and plan for conducting a subject during a period of time, are essential elements for teaching and learning activity. Therefore, in order to conduct subject successfully, syllabus and learning plan should be revised before class started. However, the revising activity is not a simple activity and sometimes becomes a complex activity that takes time. Consequently, in many cases, teachers tend to regret doing this activity; and if do so the maximal result will not be achieved. To solve this problem, Lective Gegulang™ was built to help teacher managing syllabus and lesson plan. Using Lective Gegulang, a teacher is able to work collaboratively by sharing their documents and discussing their work. Lective Gegulang is an interactive tool that adapts dynamically to high education policy and any possible changes, so, it is easy for the teacher to modify their syllabus and lesson plan; and then evaluates its consistency. Then, testing was conducted by four education experts by validating 13 features of Lective Gegulang. The result shows that 48.1% of all features are completely compliance with the syllabus and lesson plan development flow. Then, 46.1% of features are compliance with minor improvement and 5.8% of features need major improvement. Moreover, Lective Gegulang was successfully implemented in Informatics Department, Universitas Muhammadiyah Malang, with all features was fulfilling all requirements needed.
Sentiment Analysis of the 2024 Presidential Candidates Using SMOTE and Long Short Term Memory Christian Sri Kusuma Aditya; Galih Wasis Wicaksono; Hilman Abi Sarwan Heryawan
Jurnal Informatika Universitas Pamulang Vol 8, No 2 (2023): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/informatika.v8i2.32210

Abstract

Numerous political leaders participate in elections since they are a crucial component of the political process. Since electability is an issue, steps are taken to make political candidates running in general elections more electable. The media, including internet news media, has emerged as one of the key strategies for raising electability. Reader comments can be analyzed for sentiment to provide an evaluation of political figures. However, because the comments contain unstructured content, particularly in Indonesian text, it is difficult to interpret the sentiments of different comments in online news media. In this research, an analysis of public sentiment towards the 2024 presidential candidates will be carried out which is expressed through the Twitter social network. There are several stages to carry out sentiment analysis, including the stages of data collection, data preprocessing, balancing the distribution of the number of datasets, and sentiment classification using the LSTM method with word2vec feature representation. The results of this study show that the LSTM method combined with SMOTE due to the limited amount of data is able to produce a fairly good LSTM model with an average accuracy of 89.42% and a loss value of 0.24, the ideal scenario is when the accuracy is high and the loss is minimal, in which case the LSTM model only exhibits minor errors on a subset of the data. 
HERBAL LEAF CLASSIFICATION USING DEEP LEARNING MODEL EFFICIENTNETV2B0 Rakha Pradana Susilo Putra; Christian Sri Kusuma Aditya; Galih Wasis Wicaksono
JITK (Jurnal Ilmu Pengetahuan dan Teknologi Komputer) Vol. 9 No. 2 (2024): JITK Issue February 2024
Publisher : LPPM Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/jitk.v9i2.5119

Abstract

Science regarding plants has experienced significant progress, especially in the study of medicinal plants. Medicinal plants have been used in medicine and are still an important component in the world of health today. Among the various parts of the plant, the leaves are also one that can be used as medicine. However, not many people can recognize these herbal leaves directly. This is because the herbal leaves at first glance look almost the same, so it is difficult to differentiate them. The aim of this research is to classify herbal leaf images by identifying the structural features of the leaf images. The dataset in this study uses 10 classes of leaf images, namely, starfruit, guava, lime, basil, aloe vera, jackfruit, pandan, papaya, celery, and betel, where each class uses 350 images with a total of 3500 images of data. The EfficientNetV2B0 model was chosen because it has a minimalist architecture but has high effectiveness. Based on the results of research using the EffiecientNetV2B0 model, the accuracy was 99.14% and the loss value was 1.95% using test data.
Redesigning the User Interface in the Mobile-Based Ngaji.AI Application Using the Design Thinking Method Aminudin; Aldiensyah; Gita Indah Marthasari; Ilyas Nuryasin; Saiful Amien; Galih Wasis Wicaksono; Didih Rizki Chandranegara; I'anatut Thoifah
IAIC International Conference Series Vol. 4 No. 1 (2023): SEMNASTIK 2023
Publisher : IAIC Publishing

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/conferenceseries.v4i1.635

Abstract

Ngaji.AI is a mobile-based application that makes it possible to learn the recite very flexibly, wherever and whenever we can use it to learn the recite. This application is supported by artificial intelligence (AI) which provides direct and accurate assessments of how to recite Al-Quran verses properly and correctly and this application has been released on the Google Playstore platform and has been downloaded by more than 5 thousand. The Ngaji.AI application is faced with a crucial challenge, after direct observation of children and through the results of previous user input on Playstore, most of the input from users states that it needs to improve the User Interface (UI) design to make it easier to operate for children. The application of the Design Thinking method is an approach that prioritizes creativity and deep understanding of users and the problems they face and is indeed suitable for developing UI/UX of an application. Testing using the System Usability Scale (SUS) in the first test before the redesign got an average score of 50.25 and after the redesign got a significant score of 83.75. This reflects a significant increase in the level of satisfaction and ease for children in learning to recite the recite on the Ngaji.AI application.
Artificial Intelligence and Quality of Composition Verdicts in Indonesia: Lessons from New Zealand Hidayah, Nur Putri; Wicaksono, Galih Wasis; Aditya, Christian Sri Kusuma; Munarko, Yuda
Journal of Human Rights, Culture and Legal System Vol. 4 No. 1 (2024): Journal of Human Rights, Culture and Legal System
Publisher : Lembaga Contrarius Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53955/jhcls.v4i1.175

Abstract

The quality of the decision is not only related to the judge's considerations but also its suitability to the composition of the decision so that the resulting decision is not easily overturned at the level of legal action and increases public confidence in the judicial institution. This research aims to analyze the quality of judges' decisions in Indonesia in terms of the composition of the decision texts that have been made. This research uses normative legal research methods, a statutory approach, and a comparative approach. The study results show that decisions are not based on the structure of decisions determined by the Supreme Court. One of the reasons is the minimal use of AI, even though AI can help judges identify which parts of the decision structure are not yet in the decision prepared by the judge and improve them so that it is hoped that it will produce uniformity and decisions that are certain and not easily overturned. Indonesia needs to learn from New Zealand guidelines for using AI at the court and tribunal level. Judges can apply AI, some related to summarizing information and administration.
Pengolahan Korpus Dataset Audio Bacaan Al-Qur"™an Menggunakan Metode Wav2Vec 2.0 Aminudin, Aminudin; Nuryasin, Ilyas; Amien, Saiful; Wicaksono, Galih Wasis; Chandranegara, Didih Rizki; Thoifah, I'anatut; Rizky, Wahyu; Ferdiansyah, Danny; Azzahra, Kiara; Lathifah, Fildzah; Aulyah, Khairunnisa
JEPIN (Jurnal Edukasi dan Penelitian Informatika) Vol 10, No 1 (2024): Volume 10 No 1
Publisher : Program Studi Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/jp.v10i1.71576

Abstract

Pengembangan sistem otomasi pengenalan ucapan (Automatic Speech Recognition/ASR) di dalam membaca Al-Qur'an dibutuhkan korpus data audio bacaan Al-Qur'an dan beranotasi dengan transkripsi tekstual agar dapat diproses oleh algoritma machine learning. Pemrosesan Korpus dataset ini dibangun mengingat belum adanya dataset beserta pemrosesanya menggunakan metode tertentu untuk keperluan riset di dalam pengembangan ASR. Paper ini menyajikan kumpulan corpus dataset dan pengolahannya menggunakan metode Wav2Vec 2.0 dengan total 24 ribuan dataset hasil dari rekaman dari 170 santri dengan jenjang umur 4 sampai dengan 16 tahun. Pemrosesan korpus dataset dibuat mengikuti standar metode Wav2Vec 2.0 agar dapat digunakan sebagai data latih pada pemrosesan machine learning. Wav2Vec merupakan model yang dapat mempelajari representasi vektor dari masukan sinyal suara dengan proses pembelajaran self-supervised learning. Wav2Vec juga mampu menangani perbedaan aksen dan karakteristik pembaca Al-Qur'an yang bervariasi dan lebih akurat karena menggunakan deep learning. Dari hasil pengujian menggunakan parameter Precision didapatkan hasil accuracy sebesar 65.52%, precision dengan nilai 0.83 Recall dengan nilai 0.66 dan F1-Score dengan nilai 0.73 serta Word Error Rate (WER) dengan nilai 0.5. Diharapkan dengan adanya pemrosesan korpus dataset ini dapat membantu pengembangan dan riset terkait automasi sistem bacaan Al-Qur'an dengan teknik deep learning dan meningkatkan minat generasi milenial untuk belajar Al-Qur'an dengan memanfaatkan teknologi terkini.
Rancang Bangun Website Daur Hidup Batik Menggunakan Metode Waterfall Dan Kerangka Kerja Laravel Aria Maulana Eka Mahendra; Galih Wasis Wicaksono; Agus Eko Minarno
Jurnal Informatika dan Teknologi Komputer (J-ICOM) Vol 5 No 2 (2024): Jurnal Informatika dan Teknologi Komputer ( J-ICOM)
Publisher : E-Jurnal Universitas Samudra

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33059/j-icom.v5i2.8936

Abstract

Batik is a traditional art that holds great value in human life. Through a creative process that involves the selection and application of unique motifs on fabric, batik produces alluring works of art. The use of batik is not limited to special moments, but has also penetrated into the world of modern fashion. The lifecycle of batik reflects a rich cultural heritage, boundless creativity and strong human identity. This makes batik a beautiful embodiment of human life, uniqueness, and diversity in maintaining and honoring cultural traditions. The purpose of this research is to create a web-based information platform about the life cycle of batik and provide access to data managers. This system has been built using the PHP programming language, supported by the MySQL database, and using the Laravel framework. The method applied in the development of this research is the waterfall approach. The results of this study produced a web-based information platform about the batik life cycle. Through black-box testing using Selenium Web Driver, this system was proven to operate properly. Thus, it can be concluded that this system meets the needs and is acceptable to users.
Co-Authors Adi Bagus Setiawan Agus Eko Minarno Ahmad Faiz, Ahmad Ahmad Salam Rahim Al asqalani, Sheila Fitria Al Sakinata, Annisa' Al-Fatih, Solahudin Al-rizki, Muhammad Andi Aldiensyah Aldy Satria Gumilar Alfian Dwi Khoirul Annas Alfira Rizky Alimuddin Hasan Al Kabir Amiludin, Amiludin Aminudin Aminudin, Aminudin Andesti, Kiki Andjani Chaerun Nisha Andreawan Andreawana, Andreawana Anisa Dewi Anggraeni Arcelia, Allysa Sonia Aria Maulana Eka Mahendra arrafiq, ubay hakim Aulyah, Khairunnisa Azzahra, Kiara Basuki, Setio Bella Dwi Mardiana Budiono Budiono Budiono Budiono Chandranegara, Didih Rizki Cholidah, - Christian Sri Kusuma Aditya Christian Sri Kusuma Aditya Christian Sri kusuma Aditya, Christian Sri kusuma Dati Nafa Alfiana Didih Rizki Chandranegara Edo Ardhiansyah Eko Budi Cahyono Erwin Budi Setiawan Evi Dwi Wahyuni Fakhrul Nasrulloh Fath Dzulkifli Febri Ayu Fitriani Feny Aries Tanti Ferdiansyah, Danny Gilang Ramadhan Gita Indah Marthasari Hakim, Muhammad Nafi Maula Hardianto Wibowo Hari Windu Asrini Harmanto, Dani Hermansyah Adi Saputra Hilman Abi Sarwan Heryawan I'anatut Thoifah Ianatut Thoifah Intana Sari, Tiara Iqbal Fairus Zamani Kharisma Muzaki Ghufron Lathifah, Fildzah Maskur Maskur Muafika, Siti Nurmala Lailatul Muhammad Andi Al-Rizki Muhammad Farhansyah Muhammad Riadi Muhammad Risky Pratama Hermawan Muhammad Rojib Saiful Nisrina Arintia Maghfiroh Nizarullah Himawan Noor Prasetyo, Said Nur Hayatin Nur Oktaviana, Ulfah Nur Putri Hidayah Nuryasin, Ilyas Perdana, Muhammad Ilham Rakha Pradana Susilo Putra Ricky Hendrawan Rizka Nurlizah Rizky, Alfira Rizky, Wahyu Saiful Amien Saiful Arif, Mukhammad Rojib Saleh, Abd Saleha, Meisya Maulidina Sari, Tiara Intana Syuyukh, Fakhrusy Ulfah Nur Oktaviana Wahyu Budi Utomo Wildan Suharso Yuda Munarko Yufis Azhar Yunus, Nur Rohim Zakiyah Rakhmawati Zamah Sari