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All Journal Jurnal Ilmiah Teknologi dan Rekayasa Jurnal Simantec Sistemasi: Jurnal Sistem Informasi Jurnal ELTIKOM : Jurnal Teknik Elektro, Teknologi Informasi dan Komputer Justek : Jurnal Sains Dan Teknologi Jurnal Saintek Lahan Kering Jurnal Informatika Universitas Pamulang Jurnal Nasional Komputasi dan Teknologi Informasi Progresif: Jurnal Ilmiah Komputer Zonasi: Jurnal Sistem Informasi JATI EMAS (Jurnal Aplikasi Teknik dan Pengabdian Masyarakat) BERNAS: Jurnal Pengabdian Kepada Masyarakat Bakti Cendana: Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal Pengabdian kepada Masyarakat Nusantara Jurnal Generic Jurnal Restikom : Riset Teknik Informatika dan Komputer Journal Social Science And Technology For Community Service HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Journal Of Information And Technology Unimor (JITU) Jurnal Sains Teknologi dan Sistem Informasi JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Decode: Jurnal Pendidikan Teknologi Informasi Malcom: Indonesian Journal of Machine Learning and Computer Science Adopsi Teknologi dan Sistem Informasi SisInfo : Jurnal Sistem Informasi dan Informatika Majalah Ilmiah METHODA Prosiding Seminar Riset Mahasiswa Journal of Informatics and Computing JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi Jurnal Informasi Ilmu Komputer (JIIK)
Tey Seran, Krisantus Jumarto
Prodi Teknologi Informasi, Universitas Timor, Kefamenanu, Indonesia
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Sistem Pakar Diagnosa Tingkat Depresi Mahasiswa Tugas Akhir dengan Algoritma Teorema Bayes Krisantus Jumarto Tey Seran; Hevi Herlina Ullu
Progresif: Jurnal Ilmiah Komputer Vol 21, No 1 (2025): Februari
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v21i1.2454

Abstract

Students at Timor University consistently face the challenge of Final Projects. This presents a unique hurdle, and a significant number of students experience depression during this phase. Consequently, this impacts the smooth progression of guidance and the graduation rate for each semester. Therefore, early detection and prevention measures are crucial for students in the final project phase. This effort involves developing a web-based expert system to facilitate the identification of depression levels in students by their supervisors. The expert system utilizes Bayes' Theorem to provide an accuracy rating for the level of depression experienced by a student based on observable symptoms. The system development employs the Rapid Application Development (RAD) methodology. The research data comprises 28 symptom data points and three levels of depression (Mild, Moderate, Severe). System testing results demonstrate its effectiveness in measuring the depression levels of students at Timor University with an accuracy rate of 84%.Keywords: Student; Depression; Expert System; Theorem Bayes   AbstrakMahasiswa Universitas Timor selalu dihadapkan dengan persoalan Tugas Akhir. Hal ini tentunya menjadi tantangan sehingga mengakibatkan mahasiswa mengalami depresi pada saat melaksanakan tugas akhir. Akibatnya, berpengaruh dalam kelancaran proses bimbingan serta persentase kelulusan untuk setiap periode wisuda kampus. Untuk itu perlu dilakukan pencegahan (deteksi) dini bagi mahasiswa yang sedang dalam periode tugas akhir. Upaya yang dilakukan adalah dengan membangun sistem pakar berbasis website yang mempermudah dosen pembimbing dalam mendeteksi tingkat depresi yang dialami seorang mahasiswa. Sistem pakar yang dibangun menggunakan Teorema Bayes untuk memberikan nilai akurasi pada tingkatan mana seorang mahasiswa berada berdasarkan gejala yang terlihat dan Metode RAD dalam pengembangan sistem. Data penelitian yang digunakan adalah 28 data gejala dan tiga tingkatan depresi (Ringan, Sedang, Tinggi). Hasil pengujian sistem membuktikan bahwa sistem ini dapat digunakan untuk mengukur tingkat depresi mahasiswa di Universitas Timor dengan tingkat persentase 84%. Kata kunci: Mahasiswa; Depresi; Sistem Pakar; Teorema Bayes  
Penerapan Metode Forward Chaining dalam Mediagnosis Penyakit pada Ternak Babi Hevi Herlina Ullu; Krisantus Jumarto Tey Seran
Jurnal Informatika Universitas Pamulang Vol 10 No 3 (2025): JURNAL INFORMATIKA UNIVERSITAS PAMULANG
Publisher : Teknik Informatika Universitas Pamulang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32493/jiup.v10i3.35317

Abstract

The The pig population in North Central Timor (TTU) Regency in 2025 reached 111,704, making pig farming one of the main sources of livelihood for the local community. However, farmers often experience substantial losses due to high livestock mortality rates during disease outbreaks. This situation is largely attributed to the limited knowledge of pig farmers regarding disease symptoms and types, as well as limited access to information on early disease management. This study aims to develop a pig disease diagnostic application capable of identifying disease types based on observable symptoms and providing recommendations for initial treatment and preventive measures. The application was developed using the Rapid Application Development (RAD) method to accelerate system design and implementation. Meanwhile, the Forward Chaining method was applied as a fact-finding technique to infer accurate conclusions regarding disease types based on symptoms. The results of this study include a web-based pig disease diagnostic application that implements symptom tracking using forward chaining, enabling farmers to independently identify pig diseases more quickly and accurately. The developed application is expected to help reduce pig mortality rates and improve the efficiency of livestock production, particularly pig farming in TTU Regency.
KLASTERISASI MODEL PEMBELAJARAN DI UNIVERSITAS TIMOR MENGGUNAKAN METODE K-MEANS: CLUSTERING OF LEARNING MODELS AT UNIVERSITAS TIMOR USING THE K-MEANS METHOD Seran, Krisantus Jumarto Tey; Fallo, Glorita D.R.M; Ludji, Dian Grace; Chrisinta, Debora
HOAQ (High Education of Organization Archive Quality) : Jurnal Teknologi Informasi Vol. 17 No. 1 (2026): Jurnal HOAQ - Teknologi Informasi
Publisher : STIKOM Uyelindo Kupang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52972/hoaq.vol17no1.p121-130

Abstract

Penelitian ini bertujuan untuk mengidentifikasi pola model pembelajaran di Universitas Timor menggunakan metode K-Means. Lima variabel utama yang digunakan dalam klasterisasi meliputi metode pembelajaran, jenis evaluasi, ketersediaan sumber daya, tingkat kepuasan, dan fleksibilitas waktu belajar. Data primer diperoleh dari 1.018 mahasiswa aktif melalui kuesioner skala Likert. Jumlah klaster optimal ditentukan menggunakan metode Elbow, yang menghasilkan dua klaster utama. Klaster 1 didominasi oleh mahasiswa yang memilih pembelajaran offline dengan pendekatan diskusi dan evaluasi berupa tugas individu. ketersediaan sumber daya masih terbatas, tingkat kepuasan tinggi dan fleksibilitas waktu belajar sangat baik. Klaster 2 juga menunjukkan preferensi terhadap pembelajaran offline dengan metode pendekatan dosen berupa diskusi jenis evaluasinya tugas individu. Sumber daya baru sebagian yang terpenuhi , dengan tingkat kepuasan yang baik dan fleksibilitas waktu belajar yang cukup baik. Evaluasi kualitas hasil klaster menggunakan metode Silhouette Coefficient menghasilkan nilai 0,3377, yang termasuk dalam kategori Weak Structure. Hasil penelitian ini diharapkan dapat menjadi bahan pertimbangan dalam pengembangan kebijakan pembelajaran dan penyusunan kurikulum yang lebih sesuai dengan karakteristik mahasiswa.   This study aims to identify learning pattern types at the University of Timor using the K-Means clustering method. Five main variables were used in the clustering process, including learning method, type of evaluation, availability of resources, student satisfaction, and time flexibility. Primary data were obtained from 1,018 active students through a Likert-scale questionnaire. The optimal number of clusters was determined using the Elbow method, which resulted in two main clusters. Cluster 1 is dominated by students who prefer offline learning with a discussion-based approach and individual task evaluation. Resource availability is still limited, but the level of satisfaction is high, and learning time flexibility is considered very good. Cluster 2 also shows a preference for offline learning with a discussion-based teaching approach and individual task evaluation. However, resource availability is only partially met, with good satisfaction levels and fairly good time flexibility. The quality of the clustering results was evaluated using the Silhouette Coefficient method, which produced a score of 0.3377, categorized as a Weak Structure. The results of this study are expected to serve as a consideration in developing learning policies and curriculum planning that better align with student characteristics.
Analisis Perbandingan Algoritma K-Means dan K-Medoids dalam Penentuan Status Gizi Balita Krisantus Jumarto Tey Seran; Jefania Tilman Soares; Fetronela Rambu Bobu; Debora Chrisinta
JIKTEKS : Jurnal Ilmu Komputer dan Teknologi Informasi Vol. 4 No. 02 (2026): April
Publisher : Faatuatua Media Karya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70404/jikteks.v4i02.648

Abstract

Nutritional status in toddlers is an important indicator in determining child growth and development quality. Inaccurate classification of nutritional status can affect early intervention efforts. This study aims to compare the performance of K-Means and K-Medoids algorithms in clustering toddler nutritional status data at Puskesmas Betun. The dataset consists of 1,036 toddler records with variables including age, weight, height, and mid-upper arm circumference (MUAC). Data preprocessing was conducted through normalization before clustering. The performance of both algorithms was evaluated using the Davies Bouldin Index (DBI). The results show that K-Means converged in 24 iterations with a DBI value of 1.0281, while K-Medoids converged in 6 iterations with a DBI value of 1.1236. Based on the DBI evaluation, K-Means produced better clustering performance compared to K-Medoids. Therefore, K-Means is more suitable for determining toddler nutritional status in this study.
SISTEM PENDUKUNG KEPUTUSAN PEMILIHAN JENIS IKAN AIR TAWAR UNTUK BUDIDAYA DI KOLAM OELUAN MENGGUNAKAN METODE TOPSIS BERBASIS WEBSITE Yoseph Arianto Bana; Yoseph Pius Kurniawan Kelen; Krisantus Jumarto Tey Seran
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 5, No 2 (2025): JURNAL JRIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol5no2.890

Abstract

This study develops a website-based Decision Support System (DSS) for selecting the correct type of freshwater fish for cultivation in Oeluan Pond, North Central Timor Regency (TTU). This system uses the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method as a multi-criteria decision-making method, considering criteria such as pond type, soil type, pond area, location height, and water temperature. The purpose of this DSS is to assist pond managers in determining the type of freshwater fish that are economical and profitable to cultivate, as well as to support the development of fish farming in Oeluan Pond. This website-based system provides easy and flexible access anytime, anywhere, via an internet connection. The study's results are expected to enhance the effectiveness and efficiency of decision-making for freshwater fish cultivation in the area. This study also contributes to the application of information technology, especially in the field of decision support systems in the freshwater fisheries sector.Penelitian ini mengembangkan Sistem Pendukung Keputusan (SPK) berbasis website untuk pemilihan jenis ikan air tawar yang tepat untuk budidaya di Kolam Oeluan, Kabupaten Timor Tengah Utara (TTU). Sistem ini menggunakan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) sebagai metode pengambilan keputusan multi kriteria, dengan mempertimbangkan kriteria seperti jenis kolam, jenis tanah, luas kolam, ketinggian lokasi, dan suhu air. Tujuan dari SPK ini adalah untuk membantu pengelola kolam dalam menentukan jenis ikan air tawar yang ekonomis dan menguntungkan untuk dibudidayakan, serta mendukung pengembangan budidaya ikan di Kolam Oeluan. Sistem berbasis website ini memungkinkan akses yang mudah dan fleksibel kapan saja dan di mana saja melalui koneksi internet. Hasil penelitian diharapkan dapat meningkatkan efektivitas dan efisiensi dalam pengambilan keputusan budidaya ikan air tawar di daerah tersebut. Penelitian ini juga memberikan kontribusi dalam penerapan teknologi informasi khususnya dalam bidang sistem pendukung keputusan pada sektor perikanan air tawar.war
SISTEM PENDUKUNG KEPUTUSAN PENENTUAN KONDISI TANAH TERBAIK UNTUK BUDIDAYA SAYUR SAWI DI DESA LAMUDUR MENGGUNAKAN METODE TOPSIS Maria Selviyanti H Nahak; Yoseph P.K Kelen; Krisantus Jumarto Tey Seran
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 5, No 2 (2025): JURNAL JRIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol5no2.889

Abstract

This study aims to develop a website-based decision support system (DSS) to determine the optimal soil conditions for cultivating mustard greens in Lamudur Village, Weliman District, Malaka Regency. The primary issue is the limited public awareness of the suitability of land for specific commodities, resulting in suboptimal land use. The system developed utilizes the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method to process data on various soil criteria, including organic matter, soil minerals, water sources, soil slope, and previous crops. The TOPSIS method was chosen because it can provide the best solution by comparing alternative distances to positive and negative ideal solutions. The study's results show that this system can help farmers determine the most suitable soil conditions for cultivating mustard greens, improve the quality of agricultural products, and reduce the risk of crop failure. This system is also expected to serve as a reference for making informed agricultural decisions based on data and technology in rural areas.Penelitian ini bertujuan untuk mengembangkan sistem pendukung keputusan (SPK) berbasis website guna menentukan kondisi tanah terbaik untuk budidaya sayur sawi di Desa Lamudur, Kecamatan Weliman, Kabupaten Malaka. Permasalahan utama yang dihadapi adalah kurangnya pengetahuan masyarakat mengenai kesesuaian lahan untuk komoditas tertentu, sehingga sering terjadi pemanfaatan lahan yang kurang optimal. Sistem yang dikembangkan menggunakan metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) untuk mengolah data berbagai kriteria tanah, seperti unsur organik, mineral tanah, sumber air, kemiringan tanah, dan tanaman sebelumnya. Metode TOPSIS dipilih karena mampu memberikan solusi terbaik dengan membandingkan jarak alternatif terhadap solusi ideal positif dan negatif. Hasil penelitian menunjukkan bahwa sistem ini dapat membantu petani dalam menentukan kondisi tanah yang paling sesuai untuk budidaya sayur sawi, meningkatkan kualitas hasil pertanian, serta mengurangi risiko kegagalan panen. Sistem ini juga diharapkan dapat menjadi referensi dalam pengambilan keputusan pertanian berbasis data dan teknologi di wilayah pedesaan.
Design Thinking Model for Digitalizing Information Systems in Indonesia–Timor Leste Border Vocational Schools Krisantus Jumarto Tey Seran; Hevi Herlina Ullu; Ernes Josias Blegur; Nila Puspita Sari
SISTEMASI Vol 15, No 5 (2026): Sistemasi: Jurnal Sistem Informasi
Publisher : Universitas Islam Indragiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v15i5.6314

Abstract

SMK Suarna Wisata Tes is located along the border area between Indonesia and Timor-Leste. As part of Indonesia’s 3T region (Tertinggal, Terdepan, dan Terluar — disadvantaged, frontier, and outermost areas), the school represents the progress of Indonesian education in neighboring border communities, particularly for the people of the Oecusse District. One of the main limitations faced by SMK Suarna Wisata Tes is the absence of a centralized digital information system, particularly a website that can serve as an integrated platform for disseminating school information. Currently, the school relies on several social media platforms to distribute information, which requires additional time and effort for information management and communication with the public. This study developed a website for SMK Suarna Wisata Tes to facilitate the delivery and dissemination of school-related information. The digitalization of the school information system provides several benefits. Information related to teachers, students, and school profiles can be centrally stored and made accessible to the wider community. In addition, all school announcements and news can be managed in an integrated, efficient, and persistent manner. The website development implemented the Design Thinking method, which focuses on user-centered needs within the school environment. The implementation results demonstrated a positive impact on SMK Suarna Wisata Tes. Information dissemination became faster, more accurate, and more accessible to the broader community. Furthermore, data could be stored digitally and managed more efficiently. The findings of this study can serve as a reference model for the education sector in designing digital systems for schools located in border regions.
Sistem pakar diagnosis stadium rabies pada manusia menggunakan metode certainty factor berbasis web: studi kasus Puskesmas Oelolok Maria Gradiana Misa; Yoseph Pius Kurniawan Kelen; Krisantus Tey J Seran
Jurnal Ilmiah Teknologi dan Rekayasa Vol. 31 No. 1 (2026)
Publisher : Universitas Gunadarma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35760/tr.2026.v31i1.167

Abstract

Delays in the early diagnosis of rabies remains a significant issue due to the limited of public knowledge in recognizing the symptoms of the disease, resulting in delayed medical treatment. This study aims to develop a web-based expert system designed to assist in the early diagnosis of rabies in humans using the Certainty Factor (CF) method. This method is used to calculate the confidence level of the diagnosis based on the symptoms selected by the user. The system knowledge base was obtained through expert interviews and literature studies, which were represented in the form of diagnostic rules. The system is capable of providing rabies diagnosis results along with their corresponding confidence values based on the symptoms entered by users. Functional testing using the Black Box Testing method showed that all system features functioned properly. In addition, system validation was carried out by comparing the system diagnosis results with expert diagnoses through 30 testing scenarios using different symptom combinations. The test results showed 27 matching data and 3 non-matching data, resulting in a system accuracy rate of 90%. This research contributes to the implementation of the CF method in a web-based expert system to support early rabies diagnosis in a fast and measurable manner.
Klasifikasi Penerimaan Bantuan Siswa Miskin Menggunakan Naive Bayes di Wilayah Perbatasan RI-RDTL: Classification of Poor Student Assistance Recipients Using Naive Bayes in the RI–RDTL Border Region Mau, Madalena Bada; Seran, Krisantus Jumarto Tey; Ludji, Dian Grace; Maneno, Regolinda
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 6 No. 3 (2026): MALCOM July 2026
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v6i3.2689

Abstract

Program Bantuan Siswa Miskin (BSM) bertujuan membantu siswa dari keluarga kurang mampu agar tetap mengenyam pendidikan di bangku sekolah. Sampai saat ini, realita penentuan penerima bantuan masih menghadapi permasalahan subjektivitas dan kurang optimalnya pemanfaatan data riil di lapangan. Dalam penelitian ini membangun model klasifikasi menggunakan Algoritma Naïve Bayes untuk memprediksi kelayakan penerima bantuan siswa miskin tingkat SMP wilayah perbatasan Indonesia dan Timor Leste yakni Kabupaten Belu. Data yang digunakan merupakan data sekunder dari Dinas Pendidikan pada tahun 2022–2024 dan terdapat 2.607 siswa yang telah menerima bantuan BSM. Tahapan penelitian ini  meliputi preprocessing data, transformasi data kategorik, normalisasi, spilt data, model metode serta evaluasi model menggunakan teknik Holdout validation dan K-Fold cross validation. Hasil penelitian menunjukkan bahwa metode K-Fold cross validation menghasilkan rata-rata akurasi sebesar 87,69%, lebih tinggi dibandingkan Holdout validation sebesar 87,16%. Selain itu, pada fold terbaik diperoleh akurasi sebesar 99,61%, yang menunjukkan bahwa model dapat mencapai performa optimal pada kondisi tertentu dan hasil ini menegaskan bahwa model memiliki kemampuan klasifikasi yang baik dan berpotensi digunakan sebagai sistem pendukung keputusan dalam menentukan penerima bantuan pendidikan secara lebih objektif dan tepat sasaran.
Decision support system for superior livestock commodities in North Central Timor using Fuzzy Topsis method Sisilia Novita Aryenny Taek; Yoseph Pius Kurniawan Kelen; Krisantus Junarto Tey seran
Jurnal Simantec Vol 14, No 1 (2025): Jurnal Simantec Desember 2025
Publisher : Universitas Trunojoyo Madura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21107/simantec.v14i1.30059

Abstract

The livestock sector plays a crucial role in supporting regional economic development, including in North Central Timor (TTU) Regency, East Nusa Tenggara. Currently, the selection of superior livestock commodities in this area is still carried out manually, which results in slow, inefficient, and error-prone decision-making processes. Therefore, a system is needed to assist in making decisions more accurately and objectively. This study aims to develop a web-based Decision Support System (DSS) to analyze and determine superior livestock commodities in TTU Regency using the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (Fuzzy TOPSIS) method. The system was developed using the Waterfall model and implemented with PHP and MySQL. The decision criteria include productivity, market potential, production cost, environmental sustainability, and infrastructure support. The livestock alternatives considered in the analysis are cattle, goats, pigs, chickens, sheep, and horses. The implementation results show that beef cattle achieved the highest preference score of 0.76, making it the most superior livestock commodity in the region. This system provides easy access to accurate and real-time data for users and facilitates more effective and efficient decision-making. It also minimizes human error and speeds up the analysis process. With this system, stakeholders and livestock-related agencies can make better-informed decisions, improve resource management, and support sustainable development in the local livestock sector.Keywords: Decision Support System, Leading Commodities, Livestock, Fuzzy TOPSIS
Co-Authors Abi, Maria Oktavian Achmad Fariz Amsikan, Dionisius K. Anastasia Kadek Dety Lestari Banusu, Lusitania Baso, Budiman Bertha Virginia Rusae Blegur, Willem Amu Chrisinta, Debora Da Conceicao, Fedelia Da Debora Chrisinta Dewi Kristiani Asuat Dian Grace Ludji Dionisius K. Amsikan Ernes Josias Blegur Fallo, Glorita D.R.M Fallo, Kristoforus Fetronela Rambu Bobu Fried Markus Allung Blegur Gelu, Leonard Peter Hevi Herlina Ullu Hevi Herlina Ullu Isto, Kristoforus Boik Tani'i Jefania Tilman Soares Knaofmone, Maria Letitia Kristoforus Fallo Lake, Delviana Leonard Peter Gellu Mammi Sarlota Bana Maneno, Regolinda Margaretha Baria Maria Gradiana Misa Maria Grestiana Sani Maria Ilona Silab Maria Selviyanti H Nahak Maria Yosefa Sena Marjeni Liana Nino Martins, Alfiana Fontes Matute, Alejandro Jr. V. Mau, Lidia Marselina Mau, Madalena Bada Mirja Hoar Moensaku, Emanuel Nababan, Darsono Nahak, Agustinus Yasvin Nahak, Roswita R.K. Natalia Karmila Ketmoen nggadas, carles Nikolas Abi Nila Puspita Sari Nurliah Apryanti Oktavianus Klau Lekik Patricia Gertrudis Manek Risald Sae, Ina Rai Salu, Yoana Fransiska De Cantal Sesfao, Yusmiati Farida severinus ponis Sisilia Novita Aryenny Taek Spanyoli Muriano Fuel Stefanus Lau Manek Taek, Sisilia Novita Aryenny Taitoh, Rosalinda Hoar Usnaat, Sonya Oktavia Victoria Nova Naiheli Willem Amu Blegur Willem Amu Blegur Yasinta O.L Rema Yeni M Kabnani Yoseph Arianto Bana Yoseph P. K. Kelen Yoseph Pius Kurniawan Kelen Yoseph Pius Kurniawan Kelen Yoseph Pius Kurniawan Kelen Yuliana A.T Naitkakin Yulsiani Sanam