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All Journal Techno.Com: Jurnal Teknologi Informasi Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Jurnal Media Wahana Ekonomika PREDIKSI Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Sistem dan Informatika InfoTekJar : Jurnal Nasional Informatika dan Teknologi Jaringan Sistemasi: Jurnal Sistem Informasi Jurnal Teknologi dan Sistem Komputer JOIV : International Journal on Informatics Visualization Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi SELAPARANG: Jurnal Pengabdian Masyarakat Berkemajuan MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL MANAJEMEN BISNIS Jurnal Literasiologi Abdimas Umtas : Jurnal Pengabdian kepada Masyarakat Jurnal Tekno Kompak Jurnal Ilmiah Kebidanan Indonesia (Indonesian Midwifery Scientific Journal) Jurnal Teknologi Informasi dan Multimedia Jurnal Abdidas Jurnal SASAK : Desain Visual dan Komunikasi Jurnal Bumigora Information Technology (BITe) Jurnal Pengabdian UNDIKMA Journal of Electrical Engineering and Computer (JEECOM) Science in Information Technology Letters Warta Pengabdian Andalas: Jurnal Ilmiah Pengembangan Dan Penerapan Ipteks Lumbung Inovasi: Jurnal Pengabdian Kepada Masyarakat Jurnal Silva Samalas: Journal of Forestry and Plant Science Jurnal Elkasista ADMA: Jurnal Pengabdian dan Pemberdayaan Masyarakat Jurnal PRIMED:Primary Education Journal atau Jurnal Ke-SD An Journal of Engineering, Technology and Computing (JETCom) Jurnal: International Journal of Engineering and Computer Science Applications (IJECSA) Journal of Management, Economic and Accounting (JMEA) Insand Comtech : Information Science and Computer Technology Journal International Journal of Engineering Continuity Valid Jurnal Pengabdian Journal of Economics and Management Scienties Jurnal Riset Rumpun Ilmu Pendidikan (JURRIPEN) Prosiding Seminar Nasional Unimus UPGRADE: Jurnal Pendidikan Teknologi Informasi ABDIMASTEK Prosiding Seminar Nasional CORISINDO Media Pendidikan Matematika
Hairani Hairani
Universitas Bumigora, Mataram, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering 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 Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Mechanical Engineering Medicine & Pharmacology Nursing Public Health Social Sciences Other

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Web-Based Application for Toddler Nutrition Classification Using C4.5 Algorithm Hairani, Hairani; Nurhayati, Lilik; Innuddin, Muhammad
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 1 No. 2 (2022): September 2022
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v1i2.2387

Abstract

Health is something that is important for everyone, from year to year various efforts have been developed to get better and quality health. Good nutritional status for toddlers will contribute to their health and also the growth and development of toddlers. Fulfillment of nutrition in children under five years old (toddlers) is a factor that needs to be considered in maintaining health, because toddlerhood is a period of development that is vulnerable to nutritional problems. There are more than 100 toddler data registered at the Integrated Healthcare Center in Peresak Village, Narmada District, West Lombok Regency. The book contains data on toddlers along with the results of weighing which is carried out every month. However, to classify the nutritional status of toddlers, they are still going through the process of recording in a notebook by recording the measurement results and then looking at the reference table to determine their nutritional status. This method is still conventional or manual so it takes a long time to determine the nutritional status. Therefore, the solution in this study is to develop a web-based application for the classification of the nutritional status of children under five using the C4.5 method. The stages of this research consisted of problem analysis, collection of 197 instances of nutritional status datasets obtained from Integrated Healthcare Center Presak, analysis of system requirements, use case design, implementation using the C4.5 method, and performance testing based on accuracy, sensitivity, and specificity. The results of this study are a website-based application for the classification of the nutritional status of children under five using the C4.5 method. The performance of the C4.5 method in the classification of the nutritional status of toddlers using testing data as much as 20% gets an accuracy of 95%, sensitivity of 100%, and specificity of 66.6%. Thus, the C4.5 method can be used to classify the nutritional status of children under five, because it has a very good performance.
Combination of Smote and Random Forest Methods for Lung Cancer Classification Michael Lauw, Christopher; Hairani, Hairani; Saifuddin, Ilham; Ximenes Guterres, Juvinal; Maariful Huda, Muhammad; Mayadi, Mayadi
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3333

Abstract

Lung cancer is a network of cells that grow abnormally in the lungs. Lung cancer has four severity levels, namely stages 1 to 4. If lung cancer is not treated quickly, it is at risk of causing death. This research aimed to combine Synthetic Minority Over-sampling (Smote) and Random Forest methods for lung cancer classification. The method used was a combination of Smote and Random Forest. Smote was used to balance the data, while Random Forest was used to classify lung cancer data. The results showed that the combination of Smote and Random Forest methods obtained an accuracy of 94.1%, sensitivity of 94.5, and specificity of 93.7%. Meanwhile, without Smote, the accuracy is 89.1%, sensitivity is 55%, and specificity is 94.5%. The use of Smote can improve the performance of the Random Forest classification method based on accuracy and sensitivity. There was an increase of 5% in accuracy and a 39% increase in sensitivity.
Electric Vehicle Sales-Prediction Application Using Backpropagation Algorithm Based on Web Ramadhanti, Ramadhanti; Hairani, Hairani; Innuddin, Muhammad
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3388

Abstract

The accuracy of predicting future product sales is needed to minimize losses and gain profits. Inventory of goods carried out manually or improper product inventory planning causes the number of goods to accumulate due to the small number of requests, so the goods are damaged. Therefore, a sales prediction system with high accuracy is needed to assist in stocking electric vehicles. This research aimed to predict electric vehicle sales using the web-based backpropagation method. This study uses the backpropagation method to predict electric vehicle sales data from 2015 to 2022. The data is divided into 84 instances as training data and 12 instances as testing data. The result of this study was that the backpropagation method obtained a MAPE error rate of 6.25%. Thus, the backpropagation method can be used for predicting electric vehicle sales because it has a very accurate performance level.
Sentiment Analysis and Topic Modeling of Kitabisa Applications using Support Vector Machine (SVM) and Smote-Tomek Links Methods Switrayana, I Nyoman; Ashadi, Diki; Hairani, Hairani; Aminuddin, Afrig
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 2 No. 2 (2023): September 2023
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v2i2.3406

Abstract

Kitabisa is an Indonesian application that functions to raise funds online. Users can easily support various types of campaigns and donate funds to various social causes through the app. User reviews of the application are very diverse, and it is not sure whether user reviews of the application tend to be positive, neutral, or negative. This research aimed to analyze the sentiment of the Kitabisa application by modeling topics using Latent Dirichlet Allocation (LDA) and classifying user reviews using a Support Vector Machine (SVM). The scrapped dataset showed imbalanced dataset problems, so the SMOTE-Tomek Links oversampling technique was proposed. The results of this study show that using LDA produces five topics often discussed in 750 reviews. Then, the performance of SVM without using SMOTE-Tomek Links was 72% accuracy, 76% precision, 72% recall, and 64% f1 score. Meanwhile, using SMOTE-Tomek Links could significantly improve the performance, namely 98% accuracy, 98% precision, 98% recall, and 98% f1 score. Based on this research, the application of SVM achieved high performance for user sentiment classification, especially when the dataset was in a balanced state. Therefore, the SMOTE-Tomek Links oversampling technique is recommended for dealing with unbalanced sentiment datasets.
Exploring Customer Purchasing Patterns: A Study Utilizing FP-Growth Algorithm on Supermarket Transaction Data Hairani, Hairani; Ximenes Guterres, Juvinal
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 1 (2024): March 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i1.3874

Abstract

The need to analyze consumer purchasing patterns using association techniques also lies in the increasingly fierce competition in the retail market. Supermarkets face the challenge of understanding their customers' buying patterns. By utilizing association techniques, supermarkets can identify customer buying trends and quickly and appropriately adjust their strategies. Thus, analyzing consumer purchasing patterns using association techniques is no longer an option but an urgent need for supermarkets that want to survive and thrive in a changing market. Therefore, this study aimed to analyze purchasing patterns in supermarkets using the FP-Growth method to understand purchasing behavior and identify relevant patterns from transaction data. The method used in this research was the FP-Growth association method to create association rules from customer transaction data. The findings of this research were the use of the FP-Growth method in analyzing supermarket customer purchasing patterns, which obtained 10 association rules for 2 itemsets and 11 association rules for 3 itemsets based on a minimum Support value of 30% and a minimum Confidence of 70%. The association rules generated by the FP-Growth method on 2 itemsets and 3 itemsets simultaneously bring up items often purchased by customers with the same pattern, namely Cooking Oil, Eggs, Flour, and Candy. This research concludes that the association rules formed can be used as a benchmark by supermarkets in preparing stock items and making strategies to increase sales for more profit.
Thesis Topic Modeling Study: Latent Dirichlet Allocation (LDA) and Machine Learning Approach Hairani, Hairani; Janhasmadja, Mengas; Tholib, Abu; Ximenes Guterres, Juvinal; Ariyanto, Yuri
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4375

Abstract

The thesis reports housed in the campus repository have yet to be analyzed to reveal valuable knowledge patterns. Analyzing trends in thesis research topics can facilitate the selection of research topics, aid in mapping research areas, and identify underexplored topics.Therefore, this research aims to model and classify thesis topics using Latent Dirichlet Allocation (LDA) and the Naïve Bayes and Support Vector Machine (SVM) methods. This study employs the LDA method for thesis topic modeling, while SVM and Naïve Bayes are used for classifying these topics. The research results show that LDA successfully modeled five of the most popular thesis topics, namely two related to computer networks, two on software engineering, and one on multimedia. For thesis topic classification, the SVM method demonstrated higher accuracy than Naïve Bayes, reaching 92.80% after the data was balanced using Synthetic Minority Oversampling Technique (SMOTE). The implication of this study is that the topic modeling approach using LDA is able to identify dominant thesis topics. In addition, the SVM classification results obtained better accuracy than Naïve Bayes in the thesis topic classification task.
Enhancing Mental Illness Predictions: Analyzing Trends Using Multiple Linear Regression and Neural Network Backpropagation Riosatria, Riosatria; Hairani, Hairani; Anggrawan, Anthony; Syahrir, Moch.
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4391

Abstract

The increasing number of mental health cases caused by various factors such as social changes, economic pressures, and technological advancements has made it difficult to accurately predict the number of cases, hindering prevention and early intervention efforts. Therefore, developing more accurate, data-driven predictive models is necessary to improve the effectiveness of prevention and intervention. This study aims to develop a predictive model for the number of mental health cases using Multiple Linear Regression and Neural Network Backpropagation methods. The study employs two predictive methods, Multiple Linear Regression and Neural Network Backpropagation to forecast future trends in the number of mental health cases. The findings reveal that the Neural Network Backpropagation method provides more accurate predictions than Multiple Linear Regression in forecasting mental health case trends. Specifically, the Neural Network Backpropagation method resulted in an MAE of 111.39 and a MAPE of 1.77%, while the Multiple Linear Regression method produced an MAE of 115.24 and a MAPE of 1.83%. Thus, the implication of this study is that the Neural Network Backpropagation method can be utilized to predict trends in the number of mental health cases due to its ability to provide highly accurate predictions.
Clustering Analysis of Umrah Pilgrim Data Based on the K-Medoid Method Huda, Dias Nabila; Anggrawan, Anthony; Hairani, Hairani
International Journal of Engineering and Computer Science Applications (IJECSA) Vol. 3 No. 2 (2024): September 2024
Publisher : Universitas Bumigora Mataram-Lombok

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/ijecsa.v3i2.4601

Abstract

The Umrah pilgrimage is becoming increasingly popular among Indonesians, with millions of participants yearly. This trend creates a need for service providers to understand the characteristics of pilgrims to improve service quality, marketing strategies, and competitiveness. Analyzing data on pilgrims helps service providers develop more effective strategies and tailor packages to match their needs, ensuring competitiveness in a growing market. This study aims to clusters Umrah pilgrims based on age, gender, district, and chosen package using the K-Medoid clustering method. This research uses the K-Medoid method for the reason that it is more resistant to noise and outliers compared to other clustering methods. The most centrally located point in the data set is called a ”medoid,” which is an object in a cluster that has the lowest difference to all other objects in the cluster. The results of this study are that the K-Medoid method successfully grouped pilgrims into three clusters: Cluster 1 with 63 members, Cluster 2 with 25 members, and Cluster 3 with 25 members. The findings indicate that the Milad Mastour package is preferred by older pilgrims, primarily from Mataram and West Lombok. The Arbain package is favored by younger pilgrims from the same regions, while adult pilgrims mostly choose the Regular package. The implication of this research is that it can provide insights for service providers to design more specific programs that align with the profiles of pilgrims based on age and district.
Penerapan Logistic Regression dan SMOTE untuk Memprediksi Atrisi Karyawan pada Imbalanced Data Vidiasari, Viviana Herlita; Hairani, Hairani; Santoso, Heroe; Amin, Farda Milanda
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5245

Abstract

Atrisi karyawan merupakan tantangan bagi perusahaan karena berdampak langsung pada meningkatnya biaya pelatihan, hilangnya pengetahuan, serta menurunnya produktivitas dan stabilitas tim. Penelitian ini bertujuan untuk membangun model prediksi atrisi karyawan menggunakan pendekatan machine learning. Dataset yang digunakan terdiri dari 1.470 data karyawan, yang telah melalui tahap preprocessing, termasuk penanganan nilai kategorikal, normalisasi, serta seleksi fitur menggunakan Recursive Feature Elimination (RFE). Untuk mengatasi ketidakseimbangan data antara kelas tidak resign dan resign, digunakan teknik SMOTE. Model logistic regression dipilih untuk mendapatkan performa terbaik. Hasil evaluasi menunjukkan bahwa model mampu memberikan hasil yang baik dengan akurasi sebesar 0.74, ROC AUC sebesar 0.82, dan recall 0.79 pada kelas minoritas. Temuan ini diharapkan dapat digunakan oleh perusahaan sebagai alat bantu dalam pengambilan keputusan untuk mengelola risiko atrisi secara lebih efektif.
Optimalisasi Hybrid Sampling pada SVM dan Ensemble Learning untuk Prediksi Churn Wijaya, Hartono; Hairani, Hairani; Vidiasari, Viviana Herlita; Amin, Farda Milanda
CORISINDO 2025 Vol. 1 (2025): Prosiding Seminar Nasional CORISINDO 2025
Publisher : CORISINDO 2025

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/corisindo.v1.5246

Abstract

Churn merupakan kondisi ketika pelanggan menghentikan penggunaan produk atau layanan suatu perusahaan, yang secara langsung berdampak terhadap penurunan pendapatan dan peningkatan biaya akuisisi pelanggan baru. Ketidakmampuan model pembelajaran mesin dalam mengenali pelanggan yang berisiko churn akibat ketidakseimbangan data menjadi tantangan utama dalam sistem prediksi churn. Penelitian ini bertujuan untuk mengembangkan model prediksi churn yang lebih akurat dan sensitif dengan mengombinasikan algoritma Support Vector Machine (SVM) dan metode ensemble (Bagging dan Stacking), disertai penerapan teknik hybrid sampling seperti SMOTE. Metode penelitian meliputi pengumpulan data, preprocessing data, pembagian data, pelatihan model dan evaluasi . Hasil penelitian menunjukkan bahwa model Stacking memberikan performa terbaik dengan akurasi dan F1-score mencapai 86%, serta nilai AUC sebesar 0,93. Fitur products number, age, dan active member teridentifikasi sebagai variabel paling berpengaruh terhadap churn. Batasan utama penelitian ini terletak pada keterbatasan sumber data dan belum dilakukannya tuning parameter secara mendalam. Penelitian ini juga memberikan kontribusi praktis bagi strategi retensi pelanggan dan dapat dikembangkan lebih lanjut dengan validasi pada dataset lintas industri dan tuning parameter yang lebih luas.
Co-Authors Abdillah, Mokhammad Nurkholis Abdul Hadi Abdurraghib Segaf Suweleh Abdurraghib Segaf Suweleh Abu Tholib Adam, M. Awaludin Adawiyah, Rabi'atul Adrianto, Muhammad Subhan Dwi Afrig Aminuddin Ahmad Ahmad Ahmad Fathoni Ahmad Zuli Amrullah Aleeka Jasmine Amelia, Bengi Amin, Farda Milanda Andani, Nazwa Putri Andi Sofyan Anas Andi, Moh syaiful Andini, Nisha Anggarawan, Anthony Anthony Anggrawan Arfa, Muhammad Arifah Ulayya Ashadi, Diki Astuti, Ni Luh Budi Ayu Dasriani, Ni Gusti Bukran Bukran Candra, M. Ade Christine Eirene Christopher Michael Lauw Christopher Michael Lauw Dadang Priyanto Dedi Aprianto Dedy Febry Rachman Dedy Febry Rahman Dekki Widiatmoko Deny Jollyta Diah Ekawati Dian Syafitri Didik Dwi Prasetya Diki Ashadi Dirgantara, Bhintang Djoko Rahardjo Donny Kurniawan Dyah Susilowati Dyah Susilowaty ED. Yunisa Mega Pasha ED. Yunisa Mega Pasha Efendi, Muhamad Masjun Eka Setiawan, Rian Putra Ezra Azzahra Fahry, Fahry Fathorazi Nur Fajri Fatimatuzzahra Fatimatuzzahra Febriana, Annisa Dwi Fikrulia, Hidayah Jihan Firdaus, Adhitya Fitra Rizki Ramdhani Galih Hendro Martono Gede Yogi Pratama Gibran Satya Nugraha Gibran Satya Nugraha Guntara, Muhammad Gusti Ayu Diah Gita Kartika Santi, I Gustiya, Sherly Dwi Guterres, Juvinal Ximenes Hadi, M Fawazi Hammad, Rifqi Hartono Wijaya Haryono Haryono Hasanah, Maulida Hasbullah Hasbullah Herawati, Baiq Candra Heru Kurnianto Tjahjono Hery Widijanto Hidayati, Diana Huda, Dias Nabila Husain Husain I Gusti Agung Ayu Hari Triandini I Nyoman Switrayana Ida Putu Andika Ifnaldi Ifnaldi Iis Sopiah Suryani Ilham Saifuddin Indah Puji Lestari Indradewa, Rhian Irawan, Dudi Isviyanti, Isviyanti Janhasmadja, Mengas Jauhari, M. Thonthowi Jupriadi, Jupriadi Juvinal Ximenes Guterres Juvinal Ximenes Guterres Juvinal Ximenes Guterres Juvinal Ximenes Guterres Kandisa, Amelia Kasiyanto Kasiyanto Kasiyanto Kasiyanto, Kasiyanto Khairan marzuki Khairil Ihsan Khasnur Hidjah Khurniawan Eko Saputro Kurniadin Abd Latif Kurniawan Kurniawan Lalu Ganda Rady Putra Lalu Zazuli Azhar Mardedi Lestari, Jumiati Indah Lilik Nurhayati lnnuddin, Muhammad M. Ade Candra M. Rasyid Ridho M. Rizki M. Thoriq Panca Mukti M.Khaerul Ihsan M.Khaerul Ihsan Maariful Huda, Muhammad Malika, Riwayati Mamay Maulana Mamay Maulana Mardedi, Lalu Zazuli Azhar Mardedi, Lalu Zazuli Azhar Mayadi Mayadi Mayadi Mayadi Mayadi, Mayadi Mayasari, Astri Melati Rosanensi Mia Nisrina Anbar Fatin Michael Lauw, Christopher Miftahul Madani Mubarak, Ahmad Nazhif Mudawil Qulub Muhamad Azwar Muhamad Azwar, Muhamad Muhamad Reza Pahlevi Muhamad Reza Pahlevi Muhamad Wisnu Alfiansyah Muhammad Arfa Muhammad Fahmi Muhammad Ghifari, Muhammad Muhammad Innuddin Muhammad Maariful Huda Muhammad Ridho Akbar Muhammad Ridho Hansyah muhammad Syahbudi, muhammad Muhammad Tahir Muhammad Turmuzi Muhammad Zulfikri Muhammad Zulfikri Muhammad Zulkarnaen Haris Mujahid Mujahid Neny Sulistianingsih Ni Made Gita Gumangsari Nindya Alifia Khumaira Nisa, Rahayu Noor Akhmad Setiawan Novitasari Tsamrotul Fuadah Nur Intan Hayati Nur Intan Hayati Nurhayati, Lilik Nurul Azmi Nurvianti, Nurvianti Nuzululnisa, Bq Nadila Pahrul Irfan Pratama, Gede Yogi Putu Tisna Putra Qososyi, Sayidina Ahmadal Rahayun Amrullah Husaini Rahman, Mochamad Farhan Caesar Rahmawati, Lela Ramadhanti Ramadhanti Ramadhanti, Ramadhanti Rangga Wijaya Regandara, Ellysia Putri Rhomdani, Rohmad Wahid Rian Putra Eka Setiawan Rifqi Hammad Rio Riswanto Simanjuntak Riosatria, Riosatria Riwayati Malika Rizki Wahyudi Robo, Salahudin rokhim utomo Rosyda, Miftahurrahma RR. Ella Evrita Hestiandari Saifuddin Zuhri Saifuddin, Ilham Saifudin, Ilham Samsul Hadi Santoso, Heroe Shudiq, Wali Ja'far Soepriyanto, Harry Sofiansyah Fadli Soni Muhsinin Sri Farida Utami Sri Winarni Sofya Sri Winarni Sofya Sudi Prayitno Sukron, Moh Sutarman Sutarman Syahrir, Moch. tadianta m., Winardi aries Teguh Bharata Adji Tri Nur Jayanti Tri Nur Jayanti Triwijoyo, Bambang Krismono Triyanna Widiyaningtyas Umi Hanifah Vidiasari, Herlita Vidiasari, Viviana Herlita Vina Vitniawati Wahyuningsih, Rr. Sri Handari Wangiyana, I Gde Adi Suryawan Wening Asih Sutrisno Wening Asih Sutrisno Widhya Aligita Widhya Aligita Widiatmoko, Dekki Wira Hendri Wiyanto, Suko Ximenes Guterres, Juvinal Yuri Ariyanto Yuri Ariyanto Zilullah Nazir Hadi