Article Per Year (5 Year)
p-Index From 2021 - 2026
4.309
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Ilmu Komputer dan Informasi JURNAL SISTEM INFORMASI BISNIS Jupiter Jurnal Teknik ITS IPTEK Journal of Proceedings Series Semantik Techno.Com: Jurnal Teknologi Informasi Jurnal Buana Informatika TELKOMNIKA (Telecommunication Computing Electronics and Control) JUTI: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika Journal of Information Systems Engineering and Business Intelligence Jurnal IPTEK Register: Jurnal Ilmiah Teknologi Sistem Informasi Jurnal Inspiration Proceeding of the Electrical Engineering Computer Science and Informatics JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Jurnal Informatika Jurnal Ilmiah FIFO Network Engineering Research Operation [NERO] Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Matrix : Jurnal Manajemen Teknologi dan Informatika Jurnal ULTIMA InfoSys Systemic: Information System and Informatics Journal Indonesian Journal of Electrical Engineering and Computer Science Journal of Applied Computer Science and Technology (JACOST) JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Melek IT: Information Technology Journal Jurnal Nasional Teknik Elektro dan Teknologi Informasi Sewagati
Siti Rochimah
Departemen Informatika Institut Teknologi Sepuluh Nopember
Arts Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mechanical Engineering Public Health Social Sciences Other

Published : 84 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 84 Documents
Search

 5   6   7   8   9 
Perbandingan Metode Random Forest, Convolutional Neural Network, dan Support Vector Machine Untuk Klasifikasi Jenis Mangga Mardianto, Ricky; Stefanie Quinevera; Rochimah, Siti
Journal of Applied Computer Science and Technology Vol 5 No 1 (2024): Juni 2024
Publisher : Indonesian Society of Applied Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52158/jacost.v5i1.742

Abstract

Mango is a fruit known as the "King of Fruit" due to its rich flavor, vast variability, and high nutritional value. Classifying mangoes based on their external appearance is the initial step in the process of identifying and categorizing mango types conventionally. The classification process can be performed by examining external features such as fruit color, shape, and size. Classifying different types of mango fruits accurately can assist researchers in developing superior varieties and also aid farmers for cultivation purposes, sales, distribution, and selecting the right varieties for local growth and weather conditions. This research conducts the classification of mango types based on color from mango images using machine learning. The study compares three methods, namely Random Forest, Support Vector Machine (SVM), and Convolutional Neural Network (CNN), to determine the best method for classifying mango types based on their images. The dataset underwent preprocessing, where image sizes were standardized to 300 x 300 pixels, and color was changed to grayscale. The dataset was then divided into training and testing data with a ratio of 70:30. Subsequently, the dataset was processed using three methods, and their accuracy results were compared. The findings indicate that the Random Forest method yielded the highest accuracy compared to the other methods, with an accuracy rate of 96%. The accuracy of the SVM method was 95%, and the accuracy of the CNN method was 33%. From these results, it can be concluded that the Random Forest method is highly effective for classifying mango types based on their image compared to SVM and CNN methods.
Software Quality Measurement for Functional Suitability, Performance Efficiency, and Reliability Characteristics Using Analytical Hierarchy Process Sarwosri, Sarwosri; Rochimah, Siti; Laili Yuhana, Umi; Balqis Hidayat, Sultana
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2441

Abstract

The quality model used in this paper is ISO 25010. Functional Suitability, Performance Efficiency, and Reliability are the characteristics to be used. The case study used is the ITS Academic Information System, and the method used for the basis of calculation is the AHP (Analytical Hierarchy Process) method. The initial stage is to make a list of questionnaire questions, which are then filled out by three stakeholders: experts, students, and developers. With the AHP method, experts will analyze the questionnaire results to determine the required weight. This weight is used to calculate the quality of the software. There are two types of software measurements: student questionnaires and developer questionnaires. These two questionnaires become data input. Automatic measurements are carried out on Time Behavior aspects, namely Response Time Testing. In the automatic measurement stage, the URL to be tested by the tester is used as data input. From this automatic measurement, we experimented with the response time of the destination URL to respond to requests and conversion results on a scale of one hundred. The final value of these two types of measurements will be used in several equations to get the final value of the quality of the software. The study results are in the form of automatic measuring instruments of software quality. The measurement results can be used as feedback in making improvements so that the quality value increases when measured. Regarding Functional Suitability, the ITS Academic Information System has provided features according to user needs. In the aspect of Performance Efficiency, the ITS Academic Information System can provide performance and performance according to user needs. Meanwhile, regarding reliability, the ITS Academic Information System can carry out a function under certain conditions and times
An evaluation model of website testing framework based on ISO 25010 performance efficiency Kurniasari, Dias Tri; Rochimah, Siti
Indonesian Journal of Electrical Engineering and Computer Science Vol 37, No 2: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v37.i2.pp1130-1139

Abstract

Testing is an important aspect of software development. Automation testing is now widely used to achieve better and more efficient results. Various automation testing frameworks are available in the market. However, one of the major challenges is determining which automation testing framework is suitable for testing. This study proposes an evaluation model for evaluating web automation testing frameworks based on seven performance efficiency factors to address this issue. The model evaluates five types of transactions commonly used on the web; CRUD, Get Massive Data, search, file upload, and file download. In addition, the tested frameworks are categorized as good, medium, and low. To measure the success of the research, expert weighting was also used. Based on the results obtained for all types of transactions, almost all classifications between the experimental results and weighting were in the same class. Although the model was found to be effective with a 100% accuracy rate, it had an accuracy rate of 80% for upload transactions. The outcomes of this study serve as a valuable reference for choosing suitable software for both tested frameworks and other software applications. In future studies focus on narrowing the selection based on not only performance but also functionality and ease of use.
Comparative analysis of genetic algorithms for automated test case generation to support software quality Hadiningrum, Tiara Rahmania; Rochimah, Siti
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i1.pp252-259

Abstract

Software testing is crucial for enhancing software quality, but designing test cases is a labor-intensive, resource-intensive, and time-consuming process. Additionally, test case designers often introduce subjectivity when creating test cases manually. To address these challenges, this paper compares three different approaches for automatically generating program branch coverage test cases: the parallel data generation algorithm (PDGA), a standard genetic algorithm (SGA), and a random test generation method. By leveraging genetic algorithms and parallel data generation techniques, these automated approaches aim to reduce the manual effort, resources, and potential biases involved in test case design, while improving the efficiency and effectiveness of achieving comprehensive branch coverage during software testing. The experimental results, conducted using five datasets with programs written in PHP, demonstrate that PDGA outperforms both SGA and random methods across various tested programs, achieving higher maximum and average coverage. Specifically, PDGA achieved an average coverage of 100% in the "calculator" program, highlighting its superior stability and efficiency. While SGA also shows good performance, it is not as optimal as PDGA, and the random method shows the lowest performance among the three. These findings underscore the potential of genetic algorithms, particularly PDGA, to enhance the coverage and quality of software testing, thereby significantly improving system reliability. 
Segmentasi Pelanggan Majalah pada Situs Web E-Commerce dengan K-Means++ dan Metode RFM Tampubolon, Andrew Lomaksan Manuel; Butar Butar, Thio Marta Elisa Yuridis; Rochimah, Siti
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 6: Desember 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024118208

Abstract

Segmentasi pelanggan merupakan salah satu metode yang dapat diterapkan untuk memaksimalkan peluang bisnis. Hal tersebut dapat membantu bisnis agar tetap kompetitif dalam persaingan pasar. Penerapan Artificial Intelligence (AI) dapat membantu dalam memberikan pemahaman kepada pelaku bisnis tentang segmentasi pelanggan berdasarkan riwayat transaksi. Penelitian ini menerapkan metode Recency, Frequency, and Monetary (RFM) yang dipadukan dengan algoritma clustering K-Means++ untuk melakukan segmentasi pelanggan. Silhouette score menjadi indikator pemilihan nilai k yang paling optimal dalam menentukan jumlah cluster. Kerangka kerja CRISP-DM yang digunakan dalam makalah ini juga membantu mempertahankan proses analisis yang konsisten. Pendekatan statistik sederhana ddigunakan untuk mengklasifikasikan setiap fitur dalam RFM menjadi label low, medium, dan high dalam hal menangkap pola segmentasi pelanggan. Hasil eksperimen menunjukkan nilai k = 3 sebagai yang paling optimal berdasarkan nilai WSS sebesar 843,214747 dan silhouette score sebesar 0,638181. Eksperimen juga menunjukkan bahwa cluster 0 memiliki nilai RFM rata-rata sebesar 1,14 (low), 1,20 (low), dan 301.640 (low). Cluster 1 memiliki nilai RFM rata-rata sebesar 249,61 (high), 2,62 (medium), dan 799,934 (medium). Cluster 2 memiliki nilai RFM rata-rata sebesar 233,01 (medium), 6,41 (high), dan 2018,088 (high).   Abstract Customer segmentation is one method that can be applied to maximize business opportunities. It can help businesses remain competitive in the market competition. The application of Artificial Intelligence (AI) can assist in providing business stakeholders with an understanding of customer segmentation based on transaction history. This study applies the Recency, Frequency, and Monetary (RFM) method combined with the K-Means++ clustering algorithm for customer segmentation. The Silhouette score serves as an indicator for selecting the most optimal value of k to determine the number of clusters. The CRISP-DM framework used in this paper also helps maintain a consistent analysis process. A simple statistical approach is used to classify each RFM feature into low, medium, and high labels to capture customer segmentation patterns. Experimental results show that k = 3 is the most optimal value based on a WSS value of 843.214747 and a silhouette score of 0.638181. The experiments also indicate that Cluster 0 has average RFM values of 1.14 (low), 1.20 (low), and 301,640 (low). Cluster 1 has average RFM values of 249.61 (high), 2.62 (medium), and 799,934 (medium). Cluster 2 has average RFM values of 233.01 (medium), 6.41 (high), and 2018.088 (high).
Pelatihan Pembuatan Media Pembelajaran Interaktif untuk Guru Pendidikan Anak Usia Dini Dengan Canva Sarwosri, Sarwosri; Rochimah, Siti; Yuhana, Umi Laili; Oranova, Daniel; Akbar, Rizky Januar; Nuralamsyah, Bintang
Sewagati Vol 9 No 1 (2025)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v9i1.2215

Abstract

Pada era modern kini, teknologi menjadi salah satu peran penting dalam kehidupan sehari-hari. Penguasaan teknologi menjadi kemampuan yang harus dimiliki berbagai lapisan masyarakat, tak terkecuali dalam bidang pendidikan. Semua tingkat pendidikan terdampak arus perkembangan teknologi, bahkan mencapai tingkat pendidikan anak usia dini. Melihat urgensi ini, penting bagi guru PAUD untuk memanfaatkan teknologi dalam kegiatan belajar mengajar. Salah satu pemanfaatan teknologi adalah dengan pembuatan media pembelajaran yang interaktif menggunakan Canva. Dengan memiliki kemampuan ini, diharapkan kualitas kegiatan belajar mengajar akan meningkat, mengingat metode belajar yang interaktif dan menarik menjadi salah satu kunci kesuksesan dari proses belajar anak pada usia dini. Laboratorium Rekayasa Perangkat Lunak mengadakan Pelatihan Pembuatan Media Pembelajaran Interaktif untuk Guru Pendidikan Anak Usia Dini. Jumlah peserta yang mengikuti pelatihan adalah 19 orang. Pelatihan dilakukan secara luring bertempat di Laboratorium Algoritma dan Pemrograman 2, Departemen Teknik Informatika ITS. Materi pelatihan yang diberikan meliputi pengenalan desain grafis dan canva, pembuatan kolase foto, pembuatan poster acara, dan pembuatan jadwal piket kelas. Pengabdian ini berhasil dilakukan dan dapat menjadi bentuk kontribusi ITS terhadap perkembangan pendidikan di Indonesia.
Pelatihan Pembuatan Media Pembelajaran Interaktif Berbasis Video untuk Guru Pendidikan Anak Usia Dini Nuralamsyah, Bintang; Sarwosri, Sarwosri; Rochimah, Siti; Yuhana, Umi Laili; Siahaan, Daniel Oranova; Akbar, Rizky Januar
Sewagati Vol 9 No 1 (2025)
Publisher : Pusat Publikasi ITS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12962/j26139960.v9i1.2593

Abstract

Perkembangan teknologi yang pesat membuat setiap individu tak terkecuali tenaga pendidik untuk berkembang mengikutinya. Dalam perkembangan teknologi, tenaga pendidik diuji keterampilannya dalam membuat media pembelajaran yang mampu menarik perhatian dan berkulitas bagi para peserta didik. Disisi lain, pentingnya media pembelajaran sebagai media penyampaian ilmu menjadi salah satu fokus yang perlu mendapatkan perhatian khusus demi memfasilitasi perkembangan anak terutama pada masa emas perkembangan anak usia dini. Terbentuklah pelatihan guna meningkatkan ketrampilan tenaga pendidik dalam membuat media pembelajaran berbasis video yang interaktif dengan Laboratorium Rekayasa Perangkat Lunak sebagai panitia acara. Terdapat 19 peserta yang mengikuti pelatihan terkait dengan detail peserta merupakan Paguyuban Guru PAUD Gunung Anyar Tambak. Pelatihan berjalan secara luring di Laboratorium Pemrograman 2, Departemen Informatika ITS. Materi pelatihan meliputi dasar penyuntingan video, elemen-elemen dalam penyuntingan video, sampai membagikan video. Pengabdian masyarakat berbentuk pelatihan ini berhasil dilakukan dengan tingkat kepuasan 2,965 dari 3. Pelatihan ini merupakan bentuk kontribusi ITS terhadap peningkatan dan perkembangan pendidikan di Indonesia.
Klasifikasi Ulasan Aplikasi untuk Evolusi Perangkat Lunak: Sebuah Eksperimen Awal Mutia Rahmi Dewi; Hidayatul Munawaroh; Siti Rochimah
JITSI : Jurnal Ilmiah Teknologi Sistem Informasi Vol 4 No 1 (2023)
Publisher : SOTVI - Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/jitsi.4.1.102

Abstract

Application Store is a platform where users can download several applications and games. Users also can provide comments about related applications. These comments made as evaluation material for developers, who have not yet developed applications in the future. In previous studies, an application user assessment has been carried out based on existing taxonomies such as feature requests, information provision, information retrieval, and problem discovery by using Natural Language Processing (NLP), Text Analysis (TA) and Sentiment Analysis (SA). In this study, we propose a model using Topic Modelling (TM) and Minority Synthetic Over-Sampling Technique (SMOTE) to improve classification results. Making user reviews that previously ignored can be taken into consideration for developers in conducting software development. Topic modelling will generate list of topics that representing each review and SMOTE method can overcome the amount of imbalanced data on several tables. We also combine methods TA + NLP + SA, TA + NLP + SA + TM, and TA + NLP + SA + TM + SMOTE with J48 classifier....
Integrating Adaptive Sampling with Ensembles Model for Software Defect Prediction Yusuf, Muhammad; Haq, Arinal; Rochimah, Siti
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 10, No. 2, May 2025
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v10i2.2191

Abstract

Handling class imbalance is a challenge in software defect prediction. Imbalanced datasets can cause bias in machine learning models, hindering their ability to detect defects. This paper proposes an integration of Adaptive Synthetic Sampling (ADASYN) and ensemble learning methods to improve prediction accuracy. ADASYN enhances the handling of imbalanced data by generating synthetic samples for hard-to-classify instances. At the same time, the ensemble stacking technique leverages the strengths of multiple models to reduce bias and variance. The machine learning models used in this study are K-Nearest Neighbors (KNN), Decision Tree (DT), and Random Forest (RF). The results demonstrate that ADASYN, combined with ensemble stacking, outperforms the traditional SMOTE technique in most cases. For instance, in the Ant-1.7 dataset, ADASYN achieved a stacking accuracy of 90.60% compared to 89.32% with SMOTE. Similarly, in the Camel-1.6 dataset, ADASYN achieved 91.56%, slightly exceeding SMOTE’s 91.32%. However, SMOTE performed better in simpler models like Decision Tree for certain datasets, highlighting the importance of choosing the appropriate resampling method. Across all datasets, ensemble stacking consistently provided the highest accuracy, benefiting from ADASYN's adaptive resampling strategy. These results underscore the importance of combining advanced sampling methods with ensemble learning techniques to address class imbalance effectively. This approach improves prediction accuracy and provides a practical framework for reliable software defect prediction in real-world scenarios. Future work will explore hybrid techniques and broader evaluations across diverse datasets and classifiers.
Review Studi Literatur untuk Metode Pendeteksian God Class Prasetyo Putri, Divi Galih; Khairy, Muhammad Shulhan; Rochimah, Siti
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 3 No 4: Desember 2016
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (966.278 KB) | DOI: 10.25126/jtiik.201634111

Abstract

AbstrakCode Smell mengacu pada konsep mengenai pola atau aspek desain pada sistem perangkat lunak yang dapat menimbulkan masalah dalam proses pengembangan, penggunaan, atau perawatan sebagai dampak dari implementasi yang buruk dari desain perangkat lunak. Code Smell dapat menurunkan aspek understandability dan maintainability program. Program yang mengandung God Class juga cenderung lebih sulit untuk dirawat dibandingkan dengan program yang sama namun tidak mengandung God Class. God Class atau dapat juga disebut Blob merupakan sebuah kelas yang terlalu banyak berisi fungsionalitas didalamnya. Kelas-kelas seperti ini mengolah dan mengakses banyak informasi sehingga sulit dipahami. Pada penelitian ini akan dibahas metode-metode untuk mendeteksi adanya God Class. Selain itu juga dibandingkan kelebihan serta kekurangan metode-metode yang telah dianalisa. Dari pencarian literatur yang dilakukan, didapatkan 3 buah metode, metode pertama menggunakan cara deteksi dalam bentuk rule card, metode kedua menggunakan rule card dan catatan histori perubahan pada sebuah perangkat lunak, dan metode ketiga adalah pendeteksian berdasarkan contoh kelas yang dideteksi manual sebagai kecacatan perangkat lunak. Dari ketiga metode tersebut, metode ketiga dinilai sebagai yang terbaik berdasarkan nilai presisi dan recall-nya.Kata kunci: Blob, God Class AbstractCode smell referring to the concept about a pattern or design aspects on a software system that can make a problem in the process of development, using, or maintenance as the impact of bad implementation of software design. Code smell can lower software understandability and maintainability. A software that containing god class will be more difficult to maintain compared with a same software but doesn’t have a god class. God class, also called blob is a class that has too many functionality. A god class process and access a lot of information. On this research will be discussed methods to detect a god class. We also compared the advantage and disadvantage about analysed method. From the literature we search, there are 3 methods, first method using detection with a rule card, the second method using rule card and history changes of a software, and the third method is detection by examples classes that detected manually as a software defect. And our research result is the third method is the best method based on its precision and recall.Keywords: Blob, God Class
Co-Authors ABDUL MUNIF Achmad Arwan Achmad Arwan Ahmadiyah, Adhatus Solichah Akbar, Fawwaz Ali Akbar, Rizky Januar Aldy Sefan Rezanaldy Alexander L. Romy Alirridlo, Maulana Alqis Rausanfita Amirullah, Afif Ana Tsalitsatun Ni'mah Andhik Ampuh Yunanto Andy Rachman Anggraini, Ratih Nur Esti Arifiani, Siska Arini R. Rosyadi Arrijal Nagara Yanottama Bagus Priambodo Balqis Hidayat, Sultana Bambang Jokonowo Bayu Priyambadha Bayu Priyambadha Bintang Nuralamsyah Butar Butar, Thio Marta Elisa Yuridis Chastine Fatichah Choiru Zain Daniel Oranova Daniel Oranova Siahaan Darlis Heru Murti Darlis Herumurti Denni Aldi Ramadhani Denni Aldi Ramadhani Denni Aldi Ramadhani Diana Purwitasari Dianni Yusuf Dimas Widya Liestio Pamungkas Dini Adni Navastara, Dini Adni Diniar Nabilah Ghassani Djoko Pramono Dwi Sunaryono Dyah Sulistyowati Rahayu Eko M. Yuniarno Eko Wahyu Wibowo Endang Wahyu Pamungkas Evi Triandini F.X. Arunanto Faizal Johan Fernandes Sinaga Galang Amanda Dwi P. Hadiningrum, Tiara Rahmania Haniefardy, Addien Haq, Arinal Hengki Suhartoyo Hidayatul Munawaroh I Gede Suardika Imam Kuswardayan Jan Claes Karolita, Devi Khairy, Muhammad Shulhan Kholed Langsari Kurniasari, Dias Tri Kurniawan, Adi Kusbandono Ari Bowo Laili Yuhana, Umi Lesmideyarti, Dwi Lukman Hakim Lutfi Rizal Gozali Mardiana, Bella Dwi Mardianto, Ricky Mauridhi Hery Purnomo Mohammad Ahmaluddin Zinni, Mohammad Ahmaluddin Montolalu, Billy Muhammad Iskandar Java Muhammad Sonhaji Akbar Muhammad Yusuf Muhsin Bayu Aji Fadhillah Mutia Rahmi Dewi Nisa, Maidina Choirun Nugroho, Supeno Mardi S. Nur Fajri Azhar Nuralamsyah, Bintang Oranova, Daniel Pamungkas, Dimas Widya Liestio Pertiwi, Kharisma Monika Dian Pradanita, Windy Rahmadia Prasetyo Putri, Divi Galih Quinevera, Stefanie R. Firman Insan M. Rachman, Andy Rahmi Ika Noviana Ratih Nindyasari Relaci Aprilia Istiqomah Reza Fauzan Ridho Rahman Hariadi Ridwan, Mochammad Arief Riyanarto Sarno Rizky Januar Akbar Santoso, Bagus Jati Saptarini, Istiningdyah Sarwosri Sarwosri Sarwosri Sarwosri, - Septiyawan Rosetya Wardhana Setiawan, Wahyu Fajar Siska Arifiani Steven Joses Suhadi Lili Suhadi Lili Supeno Mardi S. Nugroho Tahara, Enrico Almer Tampubolon, Andrew Lomaksan Manuel Ulima Inas Shabrina Vico Ade Candra Widyanti Kartika Windy Rahmadia Pradanita Yanuar Risah Prayogi Yuhana, Umi Laili Yulvida, Donata Yuniarno, Eko M. Yusuf, Dianni Zulhaydar Fairozal Akbar