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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) MANAJEMEN HUTAN TROPIKA Journal of Tropical Forest Management Jurnal Ilmu Pertanian Indonesia Jurnal Penyuluhan MEDIA KONSERVASI Jurnal Manajemen dan Agribisnis FORUM STATISTIKA DAN KOMPUTASI Forum Pasca Sarjana Media Gizi dan Keluarga Buletin Peternakan Media Statistika Jurnal Manajemen Teknologi IPTEK The Journal for Technology and Science CAUCHY: Jurnal Matematika Murni dan Aplikasi Jurnal Ilmu Komunikasi Journal The Winners Journal of Economics, Business, & Accountancy Ventura Gadjah Mada International Journal of Business JAM : Jurnal Aplikasi Manajemen Journal of the Indonesian Mathematical Society Jurnal RISET Geologi dan Pertambangan Journal of Regional and City Planning JUITA : Jurnal Informatika Jurnal Pengelolaan Sumberdaya Alam dan Lingkungan (Journal of Natural Resources and Environmental Management) Binus Business Review JURNAL HAMA DAN PENYAKIT TUMBUHAN TROPIKA Journal of Economic Education Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Informatika Pertanian BAREKENG: Jurnal Ilmu Matematika dan Terapan JTAM (Jurnal Teori dan Aplikasi Matematika) Agrisocionomics: Jurnal Sosial Ekonomi Pertanian Jurnal Kebijakan Sosial Ekonomi Kelautan dan Perikanan KEK (Kajian Ekonomi dan Keuangan) STI Policy and Management Journal JURNAL PANGAN FIBONACCI: Jurnal Pendidikan Matematika dan Matematika InPrime: Indonesian Journal Of Pure And Applied Mathematics ESTIMASI: Journal of Statistics and Its Application Jurnal Statistika dan Matematika (Statmat) MEANS (Media Informasi Analisa dan Sistem) Jurnal Risalah Kebijakan Pertanian dan Lingkungan BISNIS & BIROKRASI: Jurnal Ilmu Administrasi dan Organisasi JURNAL ILMIAH GLOBAL EDUCATION Malcom: Indonesian Journal of Machine Learning and Computer Science Xplore: Journal of Statistics Statistika Scientific Journal of Informatics Journal of Mathematics, Computation and Statistics (JMATHCOS) Society Media Penelitian dan Pengembangan Kesehatan Indonesian Journal of Statistics and Its Applications Journal on Mathematics Education eJEBA
ASEP SAEFUDDIN
Department Of Statistics And Data Science, School Of Data Science, Mathematics And Informatics, IPB University, Bogor
Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Medicine & Pharmacology Physics Social Sciences Transportation Veterinary Other

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N-Level Structural Equation Models (nSEM): The Effect of Sample Size on the Parameter Estimation in Latent Random-Intercept Model Eminita, Viarti; Saefuddin, Asep; Sadik, Kusman; Syafitri, Utami Dyah
InPrime: Indonesian Journal of Pure and Applied Mathematics Vol. 6 No. 1 (2024)
Publisher : Department of Mathematics, Faculty of Sciences and Technology, UIN Syarif Hidayatullah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15408/inprime.v6i1.38914

Abstract

Multilevel Structural Equation Modeling (MSEM) is claimed to address hierarchical data structures and latent response variables, but it becomes unstable with an increasing number of levels. N-Level SEM (nSEM) is an SEM framework designed to handle a growing number of levels in the model. The nSEM framework uses the Maximum Likelihood Estimation (MLE) method for parameter estimation, which requires a large sample size and correct model specification. Therefore, it is essential to consider the necessary minimal sample size to ensure accurate and efficient parameter estimation in the nSEM model. This study examined how sample size affects the performance of parameter estimators in nSEM models. We propose a method to evaluate the effect of many environments to estimate the results of factor loadings and environmental variance produced by the model. In addition, we also assess the impact of environment size on the estimation results of factor loadings and individual variance. The results were then applied to actual data on student mathematics learning motivation in Depok. The findings show that neither the number of environments nor the size of the environment affects the performance of fixed parameter estimation in the nSEM model. nSEM indicates excellent performance in estimating environmental variance at level 2 when the number of environments increases. Conversely, increasing the size of the environment worsens the performance of estimating individual variance parameters. Overall, the nSEM framework for the latent random-intercept (LatenRI) model performs well with increasing sample sizes. The application data on LatenRI models show almost similar estimation results.Keywords: hierarchical data; latent random intercept model; multilevel structural equation modeling; n-level structural equation modeling.AbstrakMultilevel Structural Equation Modeling (MSEM) diklaim dapat mengatasi struktur data hierarki dan variabel respons laten, namun menjadi tidak stabil dengan bertambahnya jumlah level. N-Level SEM (nSEM) adalah kerangka kerja SEM yang dirancang untuk menangani semakin banyak level dalam model. Kerangka kerja nSEM menggunakan metode Maximum Likelihood Estimation (MLE) untuk estimasi parameter, yang memerlukan ukuran sampel yang besar dan spesifikasi model yang benar. Oleh karena itu, penting untuk mempertimbangkan ukuran sampel minimal yang diperlukan untuk memastikan estimasi parameter yang akurat dan efisien dalam model nSEM. Studi ini menguji bagaimana ukuran sampel mempengaruhi kinerja penduga parameter dalam model nSEM. Kami mengusulkan metode untuk mengevaluasi pengaruh banyak lingkungan dalam memperkirakan hasil factor loadings  dan varians lingkungan yang dihasilkan oleh model. Selain itu, kami juga menilai dampak ukuran lingkungan terhadap hasil estimasi factor loadings dan varians individu. Hasilnya kemudian diterapkan pada data aktual motivasi belajar matematika siswa di Depok. Hasil menunjukkan bahwa baik jumlah lingkungan maupun ukuran lingkungan tidak mempengaruhi kinerja estimasi parameter tetap pada model nSEM. nSEM menunjukkan kinerja yang sangat baik dalam memperkirakan varians lingkungan pada level 2 ketika jumlah lingkungan meningkat. Sebaliknya, peningkatan ukuran lingkungan akan memperburuk kinerja pendugaan parameter varians individu. Secara keseluruhan, kerangka nSEM untuk model intersepsi acak laten (LatenRI) bekerja dengan baik dengan meningkatnya ukuran sampel. Data penerapan model LatenRI menunjukkan hasil estimasi yang hampir serupa.Kata Kunci: data hirarki; model intersep acak laten; model persamaan structural multilevel; model persamaan structural n-level. 2020MSC: 62D99
Support vector machine performance: simulation and rice phenology application Muradi, Hengki; Saefuddin, Asep; Sumertajaya, I Made; Soleh, Agus Mohamad; Domiri, Dede Dirgahayu
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i6.pp4878-4890

Abstract

In the case of classification, model accuracy is expected to result in correct predictions. This study aims to analyze the performance of two kinds of support vector machine (SVM) methods: the support vector machine one versus one (SVM OvO) method and the generalized multiclass support vector machine (GenSVM) method. This method will compare to the generalized linear model, namely the multinomial logistic regression (MLR) method. Simulations were conducted using SVM OvO and GenSVM methods to get an overview of the parameters affecting both methods' performance. Furthermore, the three classification methods are implemented in the case of modelling the rice phenology and tested for performance. Simulation results show that, however, the SVM OvO and GenSVM machine learning methods are sensitive to the choice of model parameters. The empirical study results show that the SVM OvO and GenSVM methods can produce satisfactory model accuracy and are comparable to the MLR method. The best rice phenology model accuracy was obtained from the SVM OvO model, where 79.20 ± 0.21 overall accuracy and 70.69 ± 0.29 kappa were obtained. This research can be continued by handling samples, especially when class members are a minority, and can also add random effects to the SVM model.
Performance Evaluation of ARIMA and LSTM Models to Handle Multi-Interventions in Automobile Production Forecasting Maghfiroh, Firda Aulia; Indahwati, Indahwati; Saefuddin, Asep
Jurnal Ilmiah Global Education Vol. 6 No. 4 (2025): JURNAL ILMIAH GLOBAL EDUCATION
Publisher : LPPM Institut Pendidikan Nusantara Global

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55681/jige.v6i4.4694

Abstract

Intervention refers to disturbances caused by internal or external variables, such as market changes, international events, or policy shifts. The dataset used in this study contains three intervention events, referred to as a multi-input intervention. The data consist of car production figures from PT Astra Daihatsu Motor obtained from the official GAIKINDO website. The forecasting task focuses on predicting PT Astra Daihatsu Motor’s production, which was influenced by three major interventions: policy changes in 2013, the impact of the COVID-19 pandemic in 2020, and the increase in SUV production in 2022. This study compares ARIMA and LSTM models for car production forecasting. The dataset covers monthly production data from January 2010 to June 2024, with a total of 174 observations. RMSE, MAPE, and SMAPE are employed as accuracy measures. Based on the testing data (May 2023–June 2024), the results show that the LSTM model outperforms ARIMA in capturing trend patterns, with lower error values of RMSE (4587.65), MAPE (10.37), and SMAPE (10.39), compared to ARIMA with RMSE (5059.48), MAPE (11.59), and SMAPE (10.50). Accordingly, LSTM represents a relevant and robust modeling alternative for production forecasting in operational decision-making, owing to its flexibility and strong capability in capturing complex data patterns.
EFFECTIVENESS OF DIMENSIONALITY REDUCTION METHODS ON DATA WITH NON-LINEAR RELATIONSHIPS Lukmanul Hakim; Asep Saefuddin; Kusman Sadik; Anwar Fitrianto; Bagus Sartono
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 20 No 3 (2026): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol20iss3pp2507-2522

Abstract

The phenomenon of big data presents distinct challenges in the analysis process, especially when the data contains a very large number of variables. High complexity, potential redundancy, and the risk of overfitting are major issues that must be addressed through dimensionality reduction techniques. Principal Component Analysis (PCA) is a common method effective for data with linear relationships but has limitations in identifying nonlinear patterns. This research aims to improve performance of classification by introducing autoencoder for dealing with nonlinear relationship, data noise, missing values, outliers, and data with various scales. This study employs a quantitative approach through analysis of simulated and empirical data in the form of the Village Development Index from the Central Statistics Agency, which contains variables with various measurement scales. Both dimensionality reduction methods—PCA and neural network-based autoencoders—are tested across various data scenarios. The evaluation is conducted based on their effectiveness in preserving data structure, as well as the Mean Squared Error (MSE) values in the reconstruction process. The results indicate that PCA excels in computational efficiency and accuracy for data with linear relationships. In contrast, the autoencoder demonstrates superior performance in detecting nonlinear patterns, achieving lower Mean Squared Error (MSE) values with stable MSE standard deviations. Additionally, the autoencoder proves to be more robust in handling missing values and outliers compared to PCA. The selection of dimensionality reduction methods highly depends on the characteristics of the analyzed data. Autoencoders represent a superior alternative for handling complex and nonlinear data, although they require model parameter tuning. Further research is recommended to explore the influence of network architecture and training strategies of autoencoders on dimensionality reduction performance.
Ekonomi Lokal dan Pembangunan Pedesaan di Dusun Berambai, Kalimantan Timur Arman Arman; Asep Saefuddin
Society Vol 8 No 2 (2020): Society
Publisher : Laboratorium Rekayasa Sosial, Jurusan Sosiologi, FISIP Universitas Bangka Belitung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33019/society.v8i2.202

Abstract

The role of the local economy gets eroded due to the inclusion of capitalization in rural areas. This research examines the coal mining industry's influence on the local economy's existence in Berambai Hamlet, Bukit Pariaman Village, Tenggarong Seberang Sub-district, Kutai Kartanegara Regency, East Kalimantan Province, Indonesia. This research uses qualitative research methods; meanwhile, data collection methods use field observation and in-depth interviews. Interviews were conducted in stages through a snowball sampling to strengthen the observations' results. The results show that the local economy and livelihood in Berambai Hamlet are under pressure and eroded due to coal mining activities. Livelihood products shrank drastically, especially fish and rice, due to mining waste polluting rivers and agricultural land conversion to mining areas. Furthermore, other sources of income from farmworkers are not enough to fulfill the needs. The government needs to protect their livelihoods as a driving force for the local economy by integrating nature-based life. The government needs to develop local economic potentials, such as tourism areas, crafts, and artworks. The government also needs to strengthen village institutions. It must be carried out together with mining companies seriously. Furthermore, the government needs to maintain the unity of rural spatial and spatial planning.
Evaluation of Imputation Methods for Clustering Categorical Time Series on Financial Sector Stock Data Rita Rahmawati; I Made Sumertajaya; Asep Saefuddin; Kusman Sadik
Journal of Mathematics, Computations and Statistics Vol. 9 No. 2 (2026): Volume 09 Issue 02 (June 2026)
Publisher : Jurusan Matematika FMIPA UNM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35580/Jmathcos12460

Abstract

Missing values in financial time series data can affect the information structure of the data and impact the clustering results obtained. This research aims to evaluate the performance of several time series data imputation methods on the quality of categorical time series clustering on financial sector stock data on the Indonesia Stock Exchange. The imputation methods compared include linear interpolation, spline interpolation, and Kalman smoothing. The research data is in the form of daily closing prices of 92 financial sector stocks for the period 2 January 2023 to 31 October 2025. Numerical clustering was carried out using K-Means Time Series based on Dynamic Time Warping (DTW), while categorical clustering was carried out using K-Medoids with the Gower distance measure in two categorization schemes, namely five and seven categories. Evaluation of suitability between numerical and categorical clustering was carried out using the Rand Index (RI), Fowlkes–Mallows Index (FMI), and Jaccard Index. The research results show that the imputation method produces different clustering qualities. Linear interpolation provides the best and most consistent performance compared to other methods, especially in the seven-category scheme with an RI value of 0.6417, FMI of 0.4338, and Jaccard Index of 0.3256. These results show that linear interpolation is better able to maintain the information structure of the data in the categorical time series clustering process compared to spline interpolation or Kalman smoothing.
Comparison of Ordinary Kriging and Cokriging for Spatial Estimation Based on Simulated Data Siti Mutiah; Muhammad Nur Aldi; Asep Saefuddin; Fitrah Ernawati
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 10, No 2 (2025): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v10i2.33409

Abstract

This study compares the performance of Ordinary Kriging (OK) and Cokriging (CK) methods in spatial estimation based on simulated data. Twelve scenarios are arranged based on a combination of sample size (50, 250, 500) and correlation levels between variables (ρ=0.1, 0.6, 0.9), with each scenario repeated 30 times. Spatial data are generated randomly within the geographical boundaries of Indonesia, variables are generated based on spherical variograms with nugget or sill or  dan range or ,, and model evaluation is carried out using Leave-One-Out Cross Validation (LOOCV) with RMSE and  metrics. The results show that Cokriging consistently produces more accurate estimates than Ordinary Kriging in all scenarios. In the best configuration (CK, n=500), RMSE = 1.04 and  = 0.945 were obtained, while the best performance of OK only reached RMSE = 1.06 and  = 0.873. All levels of correlation in Cokriging showed good performance, especially when the amount of data is sufficient. Therefore, Cokriging is recommended as a superior spatial interpolation method in the context of multivariate and spatial data, especially when relevant secondary information is available.
BERTopic-Based Multi-Class Topic Classification on Indonesian Shopee E-commerce Reviews Using Ensemble Learning Kevin Alifviansyah; Asep Saefuddin; Septian Rahardiantoro
CAUCHY: Jurnal Matematika Murni dan Aplikasi Vol 11, No 1 (2026): CAUCHY: JURNAL MATEMATIKA MURNI DAN APLIKASI
Publisher : Mathematics Department, Maulana Malik Ibrahim State Islamic University of Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18860/cauchy.v11i1.37941

Abstract

The rapid growth of e-commerce platforms has resulted in a large volume of unstructured user reviews, creating challenges for scalable analysis. This study proposes a multi-class topic classification framework for Indonesian Shopee application reviews by integrating BERTopic-based embedding-driven topic modeling with ensemble learning. A total of 23,956 reviews are analyzed, with BERTopic applied exclusively to 19,167 training reviews to derive eight dominant topic labels, which serve as pseudo-labels for supervised classification using CatBoost and Extra Trees. Model performance is evaluated on a held-out test set under baseline and hybrid resampling settings to address severe class imbalance. The results show that hybrid resampling substantially improves balanced accuracy, particularly for CatBoost, while ROC–AUC remains consistently high, indicating robust class discrimination. Analysis of an unlabeled 2025 dataset, used solely in a deployment-style setting, reveals semantically consistent topic distributions on unseen data. Overall, the findings demonstrate that embedding-based topic modeling combined with ensemble learning provides an effective and scalable solution for multi-class topic classification in highly imbalanced e-commerce review data, with clear separation between training, evaluation, and post-deployment analysis.
Systematic Literature Review of Competitive Advantage and Marketing Capability of Small Medium Enterprises (SMEs) Hanny Nurlatifah; Asep Saefuddin; Marimin Marimin; Heny Suwarsinah
Journal of Economics, Business, and Accountancy Ventura Vol. 24 No. 2 (2021): August - November 2021
Publisher : Universitas Hayam Wuruk Perbanas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.14414/jebav.v24i2.2797

Abstract

The discussion about the formation of competitive advantage in work organizations such as SMEs is still not widely discussed. The current literature still discusses marketing activities in general, not specifically for SMEs. This article aims to find out the factors that influence SMEs' competitive advantage and marketing capabilities. The literature review method systematically uses three stages. First planning for selecting source articles, second implementing and reporting stage. The PRISMA Literature Review Model selects articles and data visualization using VOS Viewer software. The findings of this article are the related potential relationships between marketing capabilities as forming competitive advantages for small and medium enterprises. Eleven topics are frequently discussed in a collection of journals, and the dominant words are sustainable marketing orientation, marketing, and Company Performance. The three groups can be grouped into personality development, business management, and abilities. Differences in the types of business groups and business sizes as differentiators of business performance results are not widely seen in article searches. These findings suggest further research to examine business groups' role and size in determining SMEs' competitive advantage and marketing capabilities.
Induksi Mutasi pada Stek Pucuk Anyelir (Dianthus caryophyllus Linn.) melalui Iradiasi Sinar Gamma Syarifah Iis Aisyah; Hajrial Aswidinnoor; Asep Saefuddin; Budi Marwoto; Sarsidi Sastrosumarjo
Jurnal Agronomi Indonesia (Indonesian Journal of Agronomy) Vol. 37 No. 1 (2009): Jurnal Agronomi Indonesia
Publisher : Indonesia Society of Agronomy (PERAGI) and Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (620.001 KB) | DOI: 10.24831/jai.v37i1.1396

Abstract

It has been a common knowledge that LD50 is commonly used in estimating optimal doses of gamma irradiation in a breeding program. This research was aimed at observing radiosensitivity of five carnation's genotypes to gamma irradiation, to find the LD50 of carnation's cuttings, and to obtain solid mutants from five numbers of Carnation.  For cuttings, carnation genotype number 10.8 was the most insensitive to gamma rays, whereas number 24.15 was the most sensitive one.  LD50 of carnation's cuttings were obtained around 49 -72 gray. There were 19 mutants produced from this treatment. The desired mutans were mostly produced from the treated 24.1 genotype whereas the character mutans were mostly observed in MV2 generation. Irradiation treatment on genotype 24.1 produced most stabile mutans while the less was in genotype 24.14.  The produced mutants were qualitatively different in colour and petal shape, and stabile till third generation.   Key words: LD50, gamma irradiation, induced mutation, carnation.
Co-Authors . Marzuki . Sutriyati Achmad ACHMAD . Achmad Ramzy Tadjoedin adwendi, satria june Agus M Soleh Agus Mohamad Soleh Agustifa Zea Tazliqoh Ahmad A. Mattjik Ahmad Ansori Mattjik Aji H. Wigena Aji Hamim Wigena Alif Supandi Alinda F. M. Zain Alkahfi, Cahya Ananda Shafira Anang Kurnia Andres Purmalino Ani Suryani Anik Djuraidah Arief Daryanto Arista Marlince Tamonob Arman Arman Arman Arman Arman Arman Arman Arman Arnita Arnita Azagi, Ilham Alifa Bagus Sartono Bambang Indriyanto Basita Ginting Budhi Purwandaya, Budhi Budi Marwoto Budi Susetyo Bunasor Sanim Cece Sumantri Chalid Talib Citra Jaya Daowen Zhang Dede Dirgahayu Domiri Dede Dirgahayu Domiri, Dede Dirgahayu Dewi Juliah Ratnaningsih Diah Krisnatuti Dian Handayani Dian Kusumaningrum Dian Kusumaningrum, Doni Suhartono Dudung Darusman Eka Intan Kumala Putri Embay Rohaeti Enny Kristiani Enny Kristiani Erfiani Erfiani Erfiani Eri Purnomohadi Etih Sudarnika Etty Riani Euis Sunarti Eva Z Yusuf Fatah Sulaiman Fitrah Ernawati Fitrah Ernawati Fitrianto, Anwar Frisca Rizki Ananda Fulazzaky, Tahira Gerry Alfa Dito H. R. Eddie Gurnadi HAJRIAL ASWIDINNOOR Hanny Nurlatifah Harapin Hafid H. Hardiansyah . Hardinsyah Hari Wijayanto Hartoyo, harry Hasnataeni, Yunia Hendra Prasetya Hengki Muradi Heny Suwarsinah Hermanto Siregar Hidayat Syarief Hilman Dwi Anggana Husaini . I Made Sumertajaya I Made Sumertajaya I Wayan Mangku Ida Mariati Hutabarat Indahwati Itasia Dina Sulvianti Jajang Jajang Jodi Vanden Eng Joko Affandi Joko Affandi Joko Sutrisno JOKO SUTRISNO Kevin Alifviansyah Khairil Anwar Notodiputro Kristiani, Enny Krittiya Duangmanee Kusman Sadik Lia Budimulyati Salman Lia Ratih Kusuma Dewi Lilik Noor Yuliati Lismayani Usman Luh Putu Widya Adnyani Luh Putu Widya Adnyani Lukmanul Hakim Lukmanul Hakim Lukmanul Hakim M. Yunus M. Yunus Maghfiroh, Firda Aulia Mangara Tambunan Margono Slamet Marimin , Marizsa Herlina Marliati . Marliati Marliati Mirnawati Sudarwanto Muggy David Cristian Ginzel Muhammad Nur Aidi Muhammad Nur Aldi Muradi, Hengki Musa Hubeis mutiah, siti Ni Nyoman Sawitri Nimmi Zulbainarni Ningsih, Wiwik Andriyani Lestari Ninuk Purnaningsih Nirawita Untari Nunung Nuryartono Nuramaliyah, Nuramaliyah Nurul Hidayati Nusar Hajarisman Pang S. Asngari Pien Budiyanto Prabowo Tjitropranoto Pradina, Fathia Anggriani Priyadi Kardono Purnomohadi, Eri R. Ruswandi Rahardiantoro, Septian Rahmadi Sunoko Rahmadi Sunoko Ratna Megawangi Rimun Wibowo Ristu Haiban Hirzi Rita Kusriastuti Rita Rahmawati Rizal Syarief Rizal Syarief Rizka Rahmaida Ronny Rachman Noor Rudy Priyanto S. Damanhur, Didin SANTUN R.P. SITORUS Santun R.P. Sitorus Sarah Putri Sarsidi Sastrosumarjo Sausan Nisrina Setiadi Djohar Setiawan Setiawan Siti Mutiah Siti Sundari Sitti Nurhaliza Sjafri Mangkuprawira Sjafri Mangkuprawira Soedijanto Padmowihardjo Soekirman Soekirman Soetrisno Hadi Sony Sunaryo Sri Yusnita Burhan Suhartono Suhartono . Sumardjo Sumarjo Gatot Irianto Sumartono Sumartono Sutarman Sutarman . Syafri Mangkuprawira Syafri Mangkuprawira Syarifah Iis Aisyah TADJOEDIN, ACHMAD RAMZY Tagor Alamsyah Harahap Talib, Chalid Tati Rajati Tati Suprapti Tiyas Yulita triguna, gunadi Ujang Sumarwan Umi Cahyaningsih Upik Kesumawati Hadi Utami Dyah Syafitri Viarti Eminita Wahida Ainun Mumtaza William A. Hawley Wiwik Andriyani Lestari Ningsih Yani Nurhadryani Yekti Widyaningsih Yenni Angraini Yudhistira Arie Wijaya Yuni Ros Bangun Yusuf, Eva Z Zinggara hidayat