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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Jurnal Kesehatan Lingkungan indonesia Media Statistika JURNAL SISTEM INFORMASI BISNIS Jurnal Gaussian Jurnal Statistika Universitas Muhammadiyah Semarang Jurnal Sains dan Teknologi Jurnal Simetris TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Jurnal Ilmiah Kursor Jurnal Teknologi Informasi dan Ilmu Komputer Jurnal Transformatika JUITA : Jurnal Informatika WARTA Register: Jurnal Ilmiah Teknologi Sistem Informasi Journal of Information System E-Dimas: Jurnal Pengabdian kepada Masyarakat Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research Jurnal Informatika INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Seminar Nasional Variansi (Venue Artikulasi-Riset, Inovasi, Resonansi-Teori, dan Aplikasi Statistika) Jurnal Sisfokom (Sistem Informasi dan Komputer) ILKOM Jurnal Ilmiah KOMPUTIKA - Jurnal Sistem Komputer JTP - Jurnal Teknologi Pendidikan Indonesian Journal of Community Services Journal of Applied Data Sciences Jurnal Riset Teknologi Pencegahan Pencemaran Industri Indonesian Journal of Librarianship Proceeding Biology Education Conference Media Pustakawan STATISTIKA Journal of Bioresources and Environmental Sciences Scientific Journal of Informatics
Budi Warsito
Departemen Statistika, Fakultas Sains Dan Matematika, Universitas Diponegoro
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Physics Public Health Social Sciences Other

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Co-training pseudo-labeling for text classification with support vector machine and long short-term memory Handayani, Sri; Isnanto, Rizal; Warsito, Budi
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i3.pp2158-2168

Abstract

The scarcity of labeled data may hamper training text-processing models. In response to this issue, a novel and intriguing strategy that combines the co-training method and pseudo-labeling design is applied to enhance the model's performance. This method, a component of an efficient semi-supervised learning paradigm for processing and comprehending text, is a fresh perspective in the field. The model, which combines a support vector machine (SVM) for classification and long short-term memory (LSTM) for text sequence interpretation, is a unique approach. By introducing samples that may be marginalized in the labeled data, the co-training approach could help solve the class imbalance problem by using a small amount of labeled data and the rest unlabeled. This study assesses the model's performance using a student dataset from higher education institutions to establish a threshold for each model's degree of confidence and ascertain how much the model can be generalized depending on the threshold. The SVM threshold was calculated as >=0.88, and the LSTM threshold was calculated as >=0.5 using a mixture of confidence metrics.
Application of Fuzzy Service Quality Method in Measuring Student Service Satisfaction Level Ghozali, Ahmad Lubis; Warsito, Budi; Fikri, Moh Ali
JTP - Jurnal Teknologi Pendidikan Vol. 27 No. 1 (2025): Jurnal Teknologi Pendidikan
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat, Universitas Negeri Jakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21009/jtp.v27i1.54324

Abstract

University services can be evaluated based on the quality of performance provided by faculty, administrative staff, and supporting institutional structures. Ensuring student satisfaction and minimizing complaints related to campus facilities are essential in enhancing the quality of higher education. This study aims to develop a method for measuring student satisfaction with campus services at Indramayu State Polytechnic using the fuzzy SERVQUAL technique. Data were collected through a questionnaire distributed to 100 students from three majors: Engineering, Informatics, and Nursing. The instrument used was both valid, with a 5% significance level, and reliable, as shown by a Cronbach's alpha score of 0.746. Data analysis involved comparing the gap between students’ perceptions and expectations across five service quality dimensions: tangibility, reliability, responsiveness, assurance, and empathy. The analysis revealed the largest gap in tangibility and the smallest in empathy. These findings indicate that while certain service aspects meet student expectations, others still require significant improvement. The results are expected to provide valuable input for the campus to enhance service quality and better align institutional performance with student needs. It is recommended that continuous evaluation and improvement efforts be implemented, particularly in the most critical service dimensions.
Pemberdayaan Masyarakat melalui Pendampingan Pendirian Bank Sampah di Kampung Juwono Kelurahan Mangunharjo Kecamatan Tembalang Kota Semarang Suhendi, Chrisna; Setyawan, Hendri; Warsito, Budi; Sumiyati, Sri
Indonesian Journal of Community Services Vol 7, No 1 (2025): May 2025
Publisher : LPPM Universitas Islam Sultan Agung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30659/ijocs.7.1.18-25

Abstract

Pengelolaan sampah menjadi masalah kritis di Kampung Juwono, Kelurahan Mangunharjo, Kota Semarang, yang ditandai dengan peningkatan volume sampah dan kurangnya pengelolaan yang efektif. Penelitian ini bertujuan untuk meningkatkan kesadaran masyarakat tentang pentingnya pengelolaan sampah melalui pembentukan bank sampah sebagai solusi berkelanjutan. Metode yang digunakan mencakup tahapan pra-kegiatan untuk identifikasi masalah dan kesepakatan solusi dengan pemangku kepentingan, sosialisasi untuk menanamkan konsep 3R (Reduce, Reuse, Recycle) dan pentingnya bank sampah melalui penyampaian materi, diskusi, serta sesi tanya jawab, dan tahap tindak lanjut untuk mengevaluasi pemahaman peserta, pembentukan pengurus bank sampah, serta monitoring pelaksanaan pertama penimbangan sampah. Sasaran kegiatan ini adalah ibu-ibu PKK/Dasawisma dan tokoh masyarakat di RT 2 RW 3 Kampung Juwono. Melalui pelatihan dan pembekalan, diharapkan masyarakat memiliki motivasi tinggi untuk memilah dan mengelola sampah dari sumbernya. Hasil kegiatan menunjukkan peningkatan kesadaran dan partisipasi aktif masyarakat dalam pengelolaan sampah yang berkelanjutan. Implementasi bank sampah diharapkan tidak hanya mengurangi volume sampah yang dibuang ke Tempat Pembuangan Akhir (TPA), tetapi juga mendukung kelestarian lingkungan serta memberikan manfaat ekonomi bagi warga melalui konsep 3R. Pendekatan komprehensif dan kolaboratif antara pemerintah, industri, dan masyarakat diperlukan untuk memastikan keberhasilan dan keberlanjutan program ini.Waste management has become a critical issue in Kampung Juwono, Mangunharjo Village, Semarang City, characterized by increasing waste volume and inadequate management. This study aims to raise community awareness about the importance of waste management through the establishment of a waste bank as a sustainable solution. The methods employed include pre-activity stages for problem identification and agreement on solutions with stakeholders, socialization to instill the 3R (Reduce, Reuse, Recycle) concept and the importance of a waste bank through material delivery, discussions, and Q&A sessions, and follow-up stages to evaluate participants' understanding, form the waste bank management, and monitor the initial waste weighing activity. The target of this activity is the PKK/Dasawisma members and community leaders in RT 2 RW 3 Kampung Juwono. Through training and provision of materials, the community is expected to have high motivation to sort and manage waste from its source. The activity results show an increase in awareness and active participation of the community in sustainable waste management. The implementation of the waste bank is expected not only to reduce the volume of waste disposed to the Final Disposal Site (TPA) but also to support environmental sustainability and provide economic benefits to residents through the 3R concept. A comprehensive and collaborative approach between the government, industry, and community is required to ensure the success and sustainability of this program.
Analisis Sentimen Evaluasi Mahasiswa terhadap Layanan di UNISNU Jepara menggunakan Algoritma Support Vector Machine Azizah, Noor; Wibowo, Adi; Warsito, Budi; Maori, Nadia Annisa
Simetris: Jurnal Teknik Mesin, Elektro dan Ilmu Komputer Vol 16, No 1 (2025): JURNAL SIMETRIS VOLUME 16 NO 1 TAHUN 2025
Publisher : Fakultas Teknik Universitas Muria Kudus

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24176/simet.v16i1.14540

Abstract

Peningkatan kualitas layanan di perguruan tinggi menjadi salah satu fokus utama dalam dunia pendidikan. Evaluasi layanan oleh mahasiswa sering kali mencakup komentar dalam bentuk teks bebas, sehingga memerlukan pendekatan berbasis kecerdasan buatan untuk mengolah data secara efisien dan akurat. Penelitian ini bertujuan untuk menganalisis sentimen mahasiswa terhadap tiga layanan utama di UNISNU Jepara, yaitu layanan akademik, beasiswa, dan perpustakaan, menggunakan algoritma Support Vector Machine (SVM).Penelitian ini mengikuti tahapan metodologi CRISP-DM, meliputi pemahaman bisnis, pemahaman data, persiapan data, pemodelan, evaluasi, serta penarikan kesimpulan. Data yang digunakan berasal dari hasil evaluasi berbentuk komentar terbuka yang dikumpulkan melalui sistem SIAKAD. Data tersebut diproses melalui tahapan text preprocessing sebelum diterapkan algoritma SVM untuk klasifikasi sentimen.Hasil penelitian menunjukkan bahwa algoritma SVM mampu memberikan tingkat akurasi tinggi pada analisis sentimen terhadap ketiga jenis layanan, yaitu 95,8% untuk layanan akademik, 95,7% untuk layanan beasiswa, dan 98,4% untuk layanan perpustakaan. Temuan ini mengindikasikan bahwa algoritma SVM merupakan metode yang efektif untuk analisis sentimen dalam konteks data tidak terstruktur, serta memberikan wawasan strategis yang dapat membantu perguruan tinggi meningkatkan kualitas layanan mereka..
Prediction of Performance and Emissions Diesel Engines Fueled-Biodiesel Using Artificial Neural Network (ANN) Resilient Backpropagation Algorithm (Rprop) Amrulloh, Riva; Widayat, Widayat; Warsito, Budi
International Journal of Artificial Intelligence Research Vol 8, No 2 (2024): December 2024
Publisher : STMIK Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i2.1265

Abstract

In order to increase energy security and improve environmental quality, the Indonesian Goverment set a target of 23% renewable energy mix in 2025, one of which is the Mandatory Bioediesel Program. A higher biodiesel blending ratio will affect the performance and emissions of diesel engines because biodiesel is chemically different from diesel oil. Research related to the prediction of diesel engine performance and emissions using Artificial Neural Network (ANN) has been conducted, but the author sees a research opportunity for the implementation of the ANN Resilient Backpropagation (Rprop) algorithm. The data used to create the ANN model prediction was secondary data from previous research. The model designed multi input and multi output (MIMO) with 4 input variables and 7 output variables. Model building done by varying the number of neurons and hidden layers. Model evaluation selected based on the largest coefficient of determination parameter R2  and the smallest RMSE or MAPE. The results showed that the ANN single layer 4-20-7 network architecture is the best model for predicting diesel engine performance and emissions with test data R2 , RMSE and MAPE of 0.962532, 6.699428 and 6.0% respectively, while for overall data testing has a performance of 0.982869, 3.908542 and 4.3%. The results also show that based on the ANN prediction results, the increasing biodiesel ratio can increase NOx emissions and decrease HC, CO and CO emissions2 . In terms of performance, the addition of biodiesel can increase BSFC and BP and decrease BTE. The results also show that the addition of ZnO concentration can reduce emissions while in terms of performance it will increase BTE and reduce BSFC and BP.
Prediction Modeling of Capacity Factor of Rembang Coal-Fired Steam Power Plant Based on Machine Learning to Improve the Accuracy of Primary Energy Planning Perdana, Ery; Sulardjaka, Sulardjaka; Warsito, Budi
International Journal of Artificial Intelligence Research Vol 9, No 1 (2025): June
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v9i1.1394

Abstract

The Rembang Coal-Fired Power Plant (PLTU Rembang), with a capacity of 2 x 315 MW, is a key power plant in Central Java, where fuel expenses represent the largest cost component. Accurate fuel procurement planning, which relies on projecting electricity sales, is essential to reduce these costs. This study develops and compares four machine learning-based Capacity Factor (CF) prediction models: random forest regression, support vector regression, multiple polynomial regression, and multiple linear regression. The independent variables are selected from internal and external sources using F-tests and t-tests. Among the four models, the multiple linear regression model demonstrated the smallest Mean Absolute Percentage Error (MAPE) of 7.83%. Using this model, the annual CF for PLTU Rembang in 2024-2026 is predicted to be between 82% and 84%, while the CF for February-June 2024 is expected to range from 87% to 91%. With a monthly CF prediction accuracy classified as very good (MAPE of 2.35%), these predictions are valuable for optimizing monthly fuel purchase allocations, considering initial fuel stock and target inventory age (17-30 Days of Plant Operation).
Wastewater Removal Pollutants Using Polyethylene terephthalate Media : Moving Bed Biofilm Reactor Muliyadi, Muliyadi; Purwanto, Purwanto; Sumiyati, Sri; Budiyono, Budiyono; Sudarno, Sudarno; Warsito, Budi
Jurnal Presipitasi : Media Komunikasi dan Pengembangan Teknik Lingkungan Vol 22, No 2 (2025): July 2025
Publisher : Universitas Diponegoro

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14710/presipitasi.v22i2.405-419

Abstract

The increasing wastewater necessitates innovative wastewater treatment methods, such as anaerobic MBBR with PET as media, which enhance microbial degradation and biofilm formation. The aim was to analyze the rate of degradation kinetics in anaerobic MBBR reactors for biological wastewater treatment. This research examined three factors: the BOD, COD, and TSS. Domestic wastewater was used in this study. The reactor measured 40 × 40 × 50 cm and had a thickness of 4 mm. The construction was performed using glass material. The operation was performed for 30 days. Microorganisms grew and reproduced on the surface of the plastic bottle cap media during the anaerobic bioreactor seeding process by adding as many local microorganisms as 1.6/70 liters of wastewater. The study revealed that domestic wastewater used for wastewater treatment has BOD, COD, TSS, Ammonia, and Fat contents that exceed the set threshold value. The BOD/COD ratio was 0.55. After acclimatization, the biofilm was fully developed, effectively removing organic contaminants and producing fungal polysaccharides. In conclusion, the study of substrate concentration and degradation kinetics is crucial for system design and operation, emphasizing the need for substrate optimization to enhance microbial activity.
Evaluation of Machine Learning Algorithms for Classifying User Perceptions of a Child Health Monitoring Application Rahmawati, Eka; Wibowo, Adi; Warsito, Budi
Jurnal Informatika Vol 12, No 2 (2025): October
Publisher : Universitas Bina Sarana Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31294/inf.v12i2.24639

Abstract

Child growth and development are crucial aspects that every parent should monitor carefully. Proper growth and development foster the creation of a high-quality generation for the nation’s future. Recognizing the importance of monitoring children's growth, the Indonesian Pediatric developed PrimaKu, an application designed to assist parents in tracking the children's growth and development. The application includes health guidelines, growth monitoring tools, and immunization schedules. To maximize the application’s effectiveness, it is essential to evaluate its acceptance by the community, which can be assessed through user perceptions. This study evaluates the performance of machine learning algorithms, including Random Forest, Support Vector Machine, Naive Bayes, and Decision Tree, in classifying user perceptions of the PrimaKu application. The results revealed that the Support Vector Machine model achieved the highest accuracy of 81%, followed by Random Forest at 77%, Decision Tree at 74%, and Naive Bayes at 73%. Precision, recall, and F1-score used to validate the models' performance as the evaluation metrics. The findings underscore the potential of machine learning techniques in effectively classifying user feedback, providing valuable insights for improving application development and enhancing user satisfaction. This study contributes to understanding user acceptance of digital tools for child health monitoring, paving the way for better application usability and community impact
Integrasi Framework Balanced Scorecard dan COBIT 2019 dalam Pengelolaan Help Desk pada Sistem Informasi Desk Layanan Safitri, Adila; Nugraheni, Dinar Mutiara Kusumo; Warsito, Budi
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 2 (2023): July
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i2.56181

Abstract

Sistem Informasi Desk Layanan (SIDELA) adalah aplikasi yang digunakan untuk pengelolaan pengaduan dan pengajuan mengenai layanan TI yang terdapat pada Diskominfo. Pengelolaan SIDELA masih mengalami permasalahan seperti pelaksanaan audit belum dilaksanakan, sehingga belum bisa melihat capaian dari SIDELA. Tujuan penelitian untuk mengevaluasi kapabilitas sistem tata kelola teknologi informasi menggunakan framework COBIT 2019. Metode yang digunakan adalah menggunakan framework COBIT 2019. Terdapat lima tahap dalam penelitian ini yaitu tahap perencanaan, tahap ruang lingkup, tahap pengumpulan data, tahap anlisis, dan tahap penyimpulan hasil. Pengumpulan data dilakukan dengan cara ,wawancara, dan kuesioner.  Berdasarkan hasil pemetaan, dipilih 6 domain yaitu DSS01, DSS02, DSS03, DSS04, DSS05, dan DSS06. nilai target level kapabilitas yang diharapkan pada seluruh domain yaitu mencapai Level 4 (Predictable Process). Domain DSS01 memiliki gap sebanyak 4 level, DSS02 memiliki gap sebanyak 1 level, DSS03 memiliki gap sebanyak 1 level, DSS04 memiliki gap sebanyak 1 level, DSS05 memiliki gap sebanyak 4 level, dan DSS06 memiliki gap sebanyak 0 level. Hasil yang didapatkan pada penelitian ini  dalam menggunakan kerangka kerja BSC sudah memenuhi target yang diharapkan, Namun secara COBIT 2019 masih kurang, dikarenakan masih terdapat nilai kesenjangan berdasarkan level kapabilitas. Pada penelitian selanjutnya dapat menggunakan metode pengukuran tingkat kapabilitas yang lain.
Model Prediksi Kinerja Siswa Berdasarkan Data Log LMS Menggunakan Ensemble Machine Learning Ardianti, Mifta; Nurhayati, Oky Dwi; Warsito, Budi
JST (Jurnal Sains dan Teknologi) Vol. 12 No. 3 (2023): Oktober
Publisher : Universitas Pendidikan Ganesha

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23887/jstundiksha.v12i3.59816

Abstract

Institusi pendidikan saat ini menerapkan Learning Management System (LMS) sebagai sarana pembelajaran online. LMS dapat merekam sejumlah besar data perilaku siswa pada log LMS. Data perilaku ini dapat dikumpulkan dan digunakan untuk memprediksi kinerj belajar siswa. Sehingga, diperlukan analisis yang dapat mengubah sejumlah data yang tersimpan tersebut menjadi sebuah pengetahuan yang dapat meningkatkan kualitas pengajaran pada institusi pendidikan. Pada penelitian ini, mengusulkan model prediksi kinerja belajar siswa menggunakan ensemble machine learning berdasarkan ekstraksi ciri yang berhubungan dengan interaksi siswa pada LMS. Pemodelan dilakukan dengan menerapkan tiga jenis ensemble machine learning yaitu ; bagging, boosting dan voting. Hasil penelitian menunjukkan bahwa model ensemble machine learning yaitu bagging, boosting dan voting berhasil digunakan untuk memprediksi kinerja siswa dengan accuracy sebesar 81.25% dengan percision 0.810, recall 0.812 dan f-measure 0.809 yang diperoleh model bagging. Temuan pada penelitian ini adalah ensemble machine learning dapat diterapkan sebagai model prediks kinerja siswa berdasarkan data Log LMS. Institusi pendidikan baik sekolah maupun perguruan tinggi diharapkan dapat merancang sebuah kurikulum LMS untuk meningkatkan kualitas akademik institusi tersebut. Selain itu institusi pendidikan dapat memprediksi bagaimana kinerja siswanya, sehingga dapat meningkatkan prestasi akademik.
Co-Authors . Widayat Abdul Hoyyi Adi Waridi Basyirudin Arifin Adi Wibowo Adi Wibowo Agus Rusgiyono Agus Winarno, Agus Ahmad Lubis Ghozali Ahmed, Kamil Alan Prahutama Anindita Nur Safira Arafa Rahman Aziz Arbella Maharani Putri Arief Rachman Hakim Arief Rachman Hakim Arief Rachman Hakim Aris Sugiharto Arsyil Hendra Saputra Atmaja, Dinul Darma Atur Ekharisma Dewi Aurum Anisa Salsabela Bagus Dwi Saputra Bayastura, Shahnilna Fitrasha Bayu Surarso Bimastyaji Surya Ramadhan Budiyono Budiyono Calvin, Esagu John Catur Edi Widodo Chrisna Suhendi Cintika Oktavia Di Asih I Maruddani Di Mokhammad Hakim Ilmawan Dian Mariana L Manullang Dinar Mutiara Kusumo Nugraheni Dwi Ispriyanti Dyna Marisa Khairina eka rahmawati Ekky Rosita Singgih Wigati Endang Fatmawati Endang Fatmawati Fachry Abda El Rahman Faisal Fikri Utama Faliha Muthmainah Fath Ezzati Kavabilla Fatiya Nur Umma Ferry Hermawan Fiqria Devi Ariyani Firdonsyah, Arizona Gayuh Kresnawati Gertrude, Akello Ghifar Rahman Handayani, Sri Hanif Kusumasasmita Haritsa, Rifda Tsaqifarani Harjum Muharam Hasbi Yasin Hendri Setyawan Henny Widayanti, Henny Heriyanto Hizkia Christian Putra Setiadi Indra Jaya Infan Nur Kharismawan Intan Monica Hanmastiana Jafron Wasiq Hidayat Junta Zeniarja Kadarrisman, Vincensius Gunawan Slamet Kiswanto Kiswanto M. Afif Amirillah M. Andang Novianta Maharani, Chintya Ayu Mahrus Ali Maori, Nadia Annisa Maryono Maryono Maryono Maryono Masruroh, Fitriana Maulida Najwa, Maulida Mifta Ardianti Moch. Abdul Mukid Mochamad Arief Budihardjo Moh Ali Fikri mohamad jamil muhammad shodiq Muliyadi Muliyadi Munji Hanafi Mustafid Mustafid Mustaqim Mustaqim, Mustaqim Nisa Afida Izati Noor Azizah Nur Fitriyah Nurcahyanti, Tri Meida Nurul Hidayati Oktavia, Cintika Oky Dwi Nurhayati Pandu Anggara Paul, Gudoyi M Perdana, Ery Purwanto Purwanto Puspita Kartikasari Putri, Nitami Lestari R Rizal Isnanto R. Rizal Isnanto Rachmat Gernowo Rachmat Gernowo Rahmat Gernowo Rahmatul Akbar Ratna Kencana Putri Rini Nuraini Rita Rahmawati Rita Rahmawati Riva Amrulloh Riza Rizqi Robbi Arisandi Royani, Noorhanida Rukun Santoso Rully Rahadian Safitri, Adila Salma Farah Aliyah Sang Nur Cahya Widiutama Sari, Juwita Dwinda Silvia Elsa Suryana Siti Fadhilla Femadiyanti Sri Endah Moelya Artha Sri Sumiyati Sudarno Sudarno Sudarno Sudarno Sudarno utomo Sugito Sugito Sulardjaka Sulardjaka Suparti Suparti Syafrudin Syafrudin Tarno Tarno Tarno Tarno Tatik Widiharih Tatik Widiharih Ta’fif Lukman Afandi Tri Yani Elisabeth Nababan Ummayah, Putri Qodar Vincensius Gunawan Slamet Kadarrisman Wahyul Amien Syafei Whisnumurti Adhiwibowo Wibowo, Catur Edi Widiyatmoko, Carolus Borromeus Winahyu Handayani Yanuar Yoga Prasetyawan Yundari, Yundari