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All Journal Jurnal Penelitian dan Evaluasi Pendidikan JPK (Jurnal Pendidikan Khusus) INFORMASI Efisiensi : Kajian Ilmu Administrasi Autotech: Jurnal Pendidikan Teknik Otomotif Universitas Muhammadiyah Purworejo Agroland : Jurnal Ilmu-Ilmu Pertanian Reaktor Masalah-Masalah Hukum BULETIN OSEANOGRAFI MARINA International Journal of Science and Engineering (IJSE) Perkebunan dan Lahan Tropika e-Journal Pustaka Kesehatan Elkom: Jurnal Elektronika dan Komputer Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Sains Tanah Jurnal Inspirasi Pendidikan ELTE Journal (English Languange Teaching and Education) Interest : Jurnal Ilmu Kesehatan Jurnal Pseudocode Jurnal EECCIS Techno LPPM Jurnal Agro Jurnal Pendidikan Fisika Jurnal Penelitian Ekosistem Dipterokarpa Jurnal Litbang Industri MIX : Jurnal Ilmiah Manajemen Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil Kurva S : Jurnal Keilmuan dan Aplikasi Teknik Sipil Jurnal Kajian Pembelajaran Matematika Jurnal Akta Jurnal Manajemen Jurnal Manajemen dan Bisnis Indonesia JURNAL INTEGRASI Jurnal Eksplora Informatika Jurnal Pemikiran Sosiologi Agrin : Jurnal Penelitian Pertanian Jurnal Perikanan Universitas Gadjah Mada Jurnal Penelitian Pengelolaan Daerah Aliran Sungai Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat JURNAL SILOGISME : Kajian Ilmu Matematika dan Pembelajarannya Jurnal Ilmiah Ecobuss Jurnal Kesehatan Indonesia FIRM: Journal of Management Studies Jurnal Penelitian Enjiniring Fakultas Teknik Unhas Jurnal Ilmiah Dunia Hukum PENA AKUATIKA : JURNAL ILMIAH PERIKANAN DAN KELAUTAN Jurnal Teknodik Civil Service : Jurnal Kebijakan dan Manajemen PNS ASSET: Jurnal Manajemen dan Bisnis Inobis: Jurnal Inovasi Bisnis dan Manajemen Indonesia BioEksakta : Jurnal Ilmiah Biologi Unsoed J-PEN Borneo : Jurnal Ilmu Pertanian SEMAR (Jurnal Ilmu Pengetahuan, Teknologi, dan Seni bagi Masyarakat) Civics Education And Social Science Journal (CESSJ) SKANIKA: Sistem Komputer dan Teknik Informatika Jurnal Idea Hukum Jurnal Penelitian Ekosistem Dipterokarpa Jurnal Serambi Abdimas Kresna: Jurnal Riset dan Pengabdian Masyarakat Jurnal Penyuluhan Pertanian Journal Of Business, Finance, and Economics (JBFE) Jurnal Sistem Informasi Universitas Dinamika Prosiding Konferensi Nasional PKM-CSR Matriks Teknik Sipil Jurnal Litbang Industri Jurnal Teknologi Informasi Cyberku Jurnal Manajemen dan Kewirausahaan Indonesian Journal of Cancer Jurnal Universal Technic Jurnal Ilmiah Media Agrosains Jurnal Pengabdian Masyarakat Waradin Jurnal Teknik Mesin, Industri, Elektro dan Informatika Jurnal Nusantara Berbakti Jurnal Motivasi Pendidikan dan Bahasa Jurnal Ilmiah Ekonomi dan Manajemen Jurnal Sains dan Teknologi Transformasi Masyarakat : Jurnal Inovasi Sosial dan Pengabdian Globe: Publikasi Ilmu Teknik, Teknologi Kebumian, Ilmu Perkapalan Jurnal Ticom: Technology of Information and Communication Jurnal Ekonomi Bisnis dan Kewirausahaan Referendum Dinamika Sosial: Jurnal Pengabdian Masyarakat Dan Transformasi Kesejahteraan Jurnal Bengawan Solo: Pusat Kajian Penelitian dan Pengembangan Daerah Kota Surakarta Asskruie
Purwanto Purwanto
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KLASIFIKASI NAMA OBAT TULISAN TANGAN DOKTER DENGAN METODE GLCM DAN BACKPROPAGATION NEURAL NETWORK Arrahman Arrahman; Purwanto Purwanto; Pulung Nurtantio
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 12 No 2 (2016): Jurnal Teknologi Informasi CyberKU Vol. 12, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Pharmaceutical personnel in the work in general is always associated with the reading prescription, where the required accuracy, speed, and accuracy in reading prescription to avoid medication errors. This research show how to classify the doctor's handwriting drug name. Research conducted by the image processing prescription taken by scanner. Then the image manually cropped to take 200 drug names. Refining the drug name image has done twice with median filter and wiener filter, then dilation and erosion , feature extraction with GLCM (Grey-Level Co-occurance matrix) methods to obtain data sets that will be classified by the software RapidMiner. From the test we find that Backpropagation Neural Network had more accurate than Naive Bayes and C 4.5.
PREDIKSI HASIL PENJURUSAN SISWA SEKOLAH MENENGAH ATAS DENGAN MENGGUNAKAN ALGORITMA DECISION TREE C4.5 Imam Sujaj; Purwanto Purwanto; Heribertus Himawan
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 12 No 1 (2016): Jurnal Teknologi Informasi CyberKU Vol. 12, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

The Majors is one of the placement or distribution process in the selection of high school students teaching program. In these majors, students are given the opportunity to choose majors that best matches the characteristics themselves. The accuracy in choosing majors can determine the success of student learning. In contrast, an excellent opportunity for students will be lost due to lack of inaccuracy in determining the majors. In the 2013 curriculum, majors in high school started in class X after being accepted as a student, so the school should really be able to classify students on the correct corresponding majors talents and interests of students. In studies using the C4.5 algorithm to create a predictive model results placement of students because this method has been used a lot in previous studies to predict the various cases problems with good results. This is evident from the results of the C4.5 algorithm generates a classification accuracy of 96.04% value with a precision of 95.96% class, class recall of 95.92% while the value of AUC (Area Under the Curve) of 0948 + / - 0.028 with very good category. It can be concluded that in order to predict the value of the majors C4.5 algorithm produces accuracy that is very good value.
METODE SAMPLE BOOSTRAPING PADA K-NEAREST NEIGHBOR UNTUK KLASIFIKASI STATUS DESA Eko Siswanto; Suprapedi Suprapedi; Purwanto Purwanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 1 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 1 2018
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

The Ministry of Rural Area, Remote Area Development and Transmigration divides village itself into five villages, such as, Independent Village, Advance Village, Developing Village, Remote Village and Very Extremely Remote Village. The data are based on Village Potency (Podes) in 2014 by the Ministry of Rural Area, Remote Area Development and Transmigration. It is necessary to know that the data of The Ministry of Rural Area, Remote Area Development and Transmigration can be used to predict the relationship between village development indicators and the status of villages. In this case, it means whether the indicators, which are built, can influence the status of villages or not and whether they can make the status of villages become better than before. k-Nearest Neighbor (k-NN) algorithm is a method which is used to classify toward new object based on k as the nearest neighbor. k-Nearest Neighbor (k-NN) algorithm has the strength as the effective and simple algorithm and it has been used by many problem classifications. However, it has weakness if it is used for the big dataset. It can happen because it needs higher computation time. In this research, Bootstrapping Sample method is proposed to increase the optimalization of computation accuracy and time on Boostraping Sample method. Based on this research, by using the integration of k-Nearest Neighbor (k-NN) algorithm with Bootstrapping Sample method on IPD dataset on Jepara in 2014, apparently it can increase the accuracy until 5.41 % (91.89%-97.30%) than using standard k-NN algorithm. The last, from the result of this research it can be inferred that by using the integration of K-Nearest Neighbor (k-NN) algorithm with Boostraping Sample method shows the better accuracy than using standard k-NN algorithm
PREDIKSI TINGKAT LOYALITAS PELANGGAN MENGUNAKAN ALGORITMA C4.5 BERBASIS BACKWARD ELIMINATION Syaifuddin Syaifuddin; Purwanto Purwanto; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Customer loyalty is one of the capital to maintain the company's business strategy in the long run. In thelast two decades of Customer Relationship Management (CRM) has grown to become one of the majortrends in marketing, both in education and in the world practice. CRM is a comprehensive businessstrategy of a company that enables the company to effectively manage the company's relationship with thecustomer. Automatic feature selection algorithm is used with the aim of selecting a subset of the featuresin the dataset in order to reach the maximum level of accuracy in classification. The use of data miningtechniques to predict customer loyalty combines C4.5 algorithm with feature selection BackwardElimination. C4.5 algorithm based backward elimination can improve the accuracy in the prediction ofcustomer loyalty, compared with C4.5 algorithm without feature selection. C4.5 algorithm basedbackward elimination generate income per month attribute, type of subscription, registration fee, the costof the bill, and the old subscription
PREDIKSI HARGA KOMODITAS EMAS DAN BATUBARA DI PASAR DUNIA DENGAN ALGORITMA SUPPORT VECTOR MACHINE Eko Pudjianto; Purwanto Purwanto; Catur Supriyanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 1 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Changes in commodity prices of gold and coal in the world market is very influential on the Indonesian government's policy, especially in the country's revenue in the foreign exchange sector. By predicting the price of gold and coal in the world market expected the government to determine important strategy especially in the fields of mining, trade (exports), Energy and Mineral Resources in Indonesia. By applying the method of SVM (Support Vector Machine) can be found a configuration that is able to predict the prediction of gold and coal prices in the coming period.Data processing using SVM algorithm based on k - fold validation , C (cost) and its kernel , then searched the level RMSE (root mean square error) is the smallest. RMSE is the smallest design that is used in predicting the price of gold and coal. Gold commodity price prediction method with RMSE (root mean square error) is at best 43 509 + / - 37 487 with data input 7 (seven) months earlier , k - fold 10 , C (cos ) of 0.3 and using a kernel -type dot . So the commodity price forecast gold in the world market for the period December 2013 amounted to U.S. $ 1,298.33 and for coal commodities with RMSE (root mean square error) is best at 3,185 + / - 3,591 with data input 2 (two) months earlier , k - 10 fold , C (cost) of 0.3 and using a kernel-type dot. So the prediction of coal commodity prices on the world market for the period from December 2013 is U.S. $ 81.58
KLASIFIKASI PENGADUAN MASYARAKAT MENGGUNAKAN NAIVE BAYES BERBASIS SELEKSI ATRIBUT INFORMATION GAIN Alter Lasarudin; Purwanto Purwanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 14 No 2 (2018): Jurnal Teknologi Informasi CyberKU Vol.14 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

The development of information every day is increasing. Public complaint is one form of information on the Internet is growing each day according to the number of people who make a complaint. In the management of complaints frequent errors in aduannya groupings so as to make the admin must work longer to perform grouping or classification of complaints. Such information becomes a media which is used for data mining research. One of the functions of data mining is classification. Naïve Bayes is one of the methods used for classification, one for the classification of documents or text. The classification is very useful for grouping data or documents by category. This will simplify the user data or documents in the search process. This research was conducted by applying the method Naïve Bayes for classification societies complaint data and algorithms Information Gain for the selection of attributes in order to improve the accuracy of the classification of public complaints. The test results by using 150 training data and testing the data 60 Naïve Bayes algorithm using attribute selection results without accuracy is 63.33%. whereas on testing Naïve Bayes algorithm using Information Gain attribute selection with the same data results are increasing even with k = 5. The best accuracy results found in this study was 86.67% using the selection attribute by 55.
Klasifikasi Penerbitan Surat Keputusan Tunjangan Profesi Guru Menggunakan Naive Bayes Berbasis Information Gain Rani Pratikaningtyas; Purwanto Purwanto; Moch Arief Soeleman
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Sertifikasi guru merupakan salah satu upaya pemerintah untuk meningkatkan mutu pendidikan disertai dengan peningkatan kesejahteraan guru. Namun banyaknya penerima sertifikasi yang ternyata tidak cair berpengaruh kepada laporan anggaran belanja negara dan daerah. Penelitian ini bertujuan untuk melakukan seleksi fitur dengan cara memberi bobot pada setiap atribut dari data Penerbitan Surat Keputusan Tunjagan Profesi Guru di Kota Surakarta tahun 2015, menggunakan metode information gain untuk meningkatkan akurasi pada algoritma Naïve Bayes, sehingga dapat mengklasifikaasi penerbitan surat keputusan tunjangan profesi guru dengan baik. Information gain digunakan untuk memilih atribut khususnya dalam menangani data dengan dimensi tinggi. Sedangkan untuk proses klasifikasinya menggunakan algoritma Naïve Bayes yang merupakan teknik prediksi berbasis probabilistic sederhana. Adapun atribut yang digunakan dalam eksperiman ini adalah, NUPTK, Format Bayar, Jenis PTK, Jenis Kelamin, NIP, Status Kepegawaian, Kode Sertifikasi, Area Tugas, Jenjang, JJM Mengajar, Tugas Tambahan, Tugas Mengajar, Golongan, Nama Bank, Keputusan. Hasil Eksperimen dari metode Naïve Bayes didapatkan hasil akurasi sebesar 93,31% sedangkan setelah menggunakan seleksi fitur dengan information gain didapatkan hasil akurasi sebesar 96,11%. Sehingga mengalami peningkatan akurasi sebesar 2,80%.
Identifikasi Jumlah Bibit Bandeng Menggunakan Metode K-Means Berbasis HSV Color dan Morfologi Salman Suleman; Purwanto Purwanto
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 2 (2019): Jurnal Teknologi Informasi - Jurnal CyberKU Vol. 15, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Seed milkfish is one commodity of national food security. Availability of seed milkfish as one of the major production support in the cultivation of milkfish in ponds must be fulfilled. Factors seed availability is essential in improving the commodities which impact on improving the living standard of farmers' welfare milkfish seedling cultivation. Seed fish are difficult to identify because the object is small so that farmers banding should be extra seedlings in calculating the amount of seed milkfish contained in one container. Identification of seed milkfish (milkfish seeds) one way to find out information on the number of seeds in a container milkfish. This research proposes the identification number of seeds banding using the K-Means method based on the HSV Color and morphology preprocessing. This research begins with step preprocessing, do transformasi Color original image RGB to HSV and RGB to Grayscale by the threshold value the image of S and V the Color space (Color space) HSV and morphology, the next process then feature extraction based on the area and the latter process is counting the number of seeds that are recognized as banding objects based on the results of clustering using the K-Means method. Based on the results of testing milkfish Seed identification show reached 92.70% accuracy and error rate 7:30%.
PENGENALAN VARIETAS MANGGA BERDASARKAN BENTUK DAN TEKSTUR DAUN MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK Fathorazi Nur Fajri; Purwanto Purwanto; Ricardus Anggi Pramunendar
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 2 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Pada saat ini mangga Indonesia sangat diminati oleh orang asing terlebih untuk mangga kualitas unggul seperti mangga manalagi dan gadung. Akan tetapi tak jarang masyarakat tidak mengerti atau keliru mengenali varietas mangga yang mereka tanam. Selama ini identifikasi atau pengenalan varietas mangga dilakukan dengan menggunakan mata. Hal ini pun dibutuh keahlian atau pakar dalam membedakan varietas mangga tersebut. Akan tetapi orang yang ahli mempunyai keterbatasan, tidak semua varietas mangga dapat dikenali atau diidentifikasi. Terdapat beberapa usulan model yang telah dilakukan untuk mengindentifikasi mangga dengan citra digital akan tetapi akurasi yang dihasilkan masih kurang yaitu di bawah 80 %. Selain itu masing masing peneliti hanya menggunakan satu fitur citra yaitu fitur tekstur. Penelitian ini mengunakan dataset sebanyak 300 citra daun mangga, 150 citra daun mangga varietas manalagi dan 150 citra daun gadung. Metode yang digunakan pada penelitian ini yaitu Backpropagation Neural Network (BPNN) dengan menggunakan fitur bentuk dan tekstur daun mangga. Model BPNN yang paling optimal pada penelitian ini yaitu menggunakan hidden layer = 19, learning rate = 0.9, momentum = 0.9 dan epoch = 100 dengan hasil root mean squar error (RMSE) = 0.0018. Kemudian hasil dari pengujian menggunakan citra daun mangga menghasilkan tingkat akurasi 96 %.
ALGORITMA SUPPORT VECTOR MACHINE UNTUK MEMPREDIKSI NILAI UJIAN NASIONAL Emi Rizky; Purwanto Purwanto; Heribertus Himawan
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 11 No 2 (2015): Jurnal Teknologi Informasi CyberKU Vol.11 no 2
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

In order to improve the quality of graduate education through an exam in order to compete in domestic, regional and international levels and therefore require the achievement of national standards through the National Examination (UN). produce test scores that boast with the title and can pass the National Exam, due to lack of graduates when the National Examination become routine issues annually. This problem is felt by students, parents, teachers, educational units and agencies associated with other national exams. By looking at the reasons we need a prediction to predict the value of the UN. Soft computing has several abilities one of which is a technique that can be used to predict the ability of students to acquire the methods of the National Examination Support Vector Machine (SVM) which is a branch of artificial intelligence where the processing system configuration information obtained performance model for the prediction of the National Examination the Root mean squared Error (RMSE) is the best for Indonesian was 0.713 + / - 0.173, English at 0586 + / - 0.066, and Mathematics by 0882 + / - 0188. configuration with predictions using a barometer. k-fold 10, C (cost) of 0.1 with kernel-type radial Indonesian subjects, k-fold 10, C (cost) of 0.3 with radial kernel type for the subjects of English and Mathematics.
Co-Authors . Supriyanto A. S.D. Purwantono Abadyo Abadyo Agung Budi Supangat Agung Doni Anggoro Agung Prasetyo Fitrianto Agung Teguh Setyadi Agus Somamihardja Agustina Retnoasih Ahmad Agus Saikhoni Alfa Mariana Bleszyski Alter Lasarudin Amos Amos Anastasia Intan Sawitri Andriana Yeni Oswita Aningtyas Diningrum Apriangga Rachmandinur Aprilina Purbasari Ardian Prima Putra Ardian Putra Putra Arif Rakhman Suharso Arif Rianto Budi Nugroho Ario Hendartono Arrahman Arrahman Arwan Nur Ramadhan Aryanti Virtanti Anas Asmara Indahingwati Asta Arjunoarwan Hatta Ayu Devina Putri Azam Bachur Zaidy Azis N. Bambang B. Amrulloh B.M.A.S. Anaconda Bangkara Baharinawati Wilhan Hastanti Bambang Nur C Bambang Nur Cahyaningrum Bambang Rudianto Wijonarko Budi Purnomo Budi Supono Indaryanto Catur Supriyanto Damar Irsyad Ustadz Dedy Ansari Harahap Devit Setiono Dewi Retna Indrawati Didik Setiyawan Dita Amanah Djamaluddin Djamaluddin Djoko Sudarmono Dwi Achadiani Dwi Ayu Lutfiani Amalia Dyah Hesti Wardhani Eddy Madiono Sutanto Eka Junianti Eka Oktaviani Eko Pudjianto Eko Siswanto Elly Proklamasiningsih Emi Rizky Eneng Martini Erlyna Hidyantari Erwin Sutomo Evi Thelia Sari Faiqotul Falah Faizah Wardhina Faizal Beni Akbar Fajar Hanung Basworo Fathorazi Nur Fajri Fenandi Bilian Firdaus Kamilullah Suhada Fitria Roviqowati H Hadiyanto Hardiono, Hardiono Hardjianto, Mardi Hargono Hargono Hartati S Hartmantyo Pradigto Utomo Heri Cahyono Heribertus Himawan Hijrah Faqih Ramadhan Ichwan Mochammad Buchori Iin Novitasari Imam Sujaj Intan Paramita Haty Irzal Nur, Irzal Jajang Hendar H Jatmiko Rinto Wahyudi Johan Suryo Prayogo Joko Christian Chandra Joko Tri Haryanto Kholisa Kholisa Kristian Korniadi Laksono Trisnantoro Lamhot P Manalu Lenny Sari Lili Halimah M. Shodikin Masyithoh G Meta Diana Moch Arief Soeleman Mochammad Ricky Andriyanto Mohammad Alif Riskyansah Mohammad Fathoni Mu'izzaddin Wa'addulloh Muhammad - Ramli Muhammad Aziz Muslim Muhammad Dwi Nursansyah Muhammad N. Misuari Muhammad Rasnijal Muis Nanja Muji Gunarto Mujiono Mujiono Mustofa Mustofa Nadia Sasmita Wijayanti Nana Haryanti Nilna Hidayah Nining Wahyuningrum Nirmana Fiqra Qaidahiyani Nufus M Nunun Tri Widarwati Nur Sakinah Asaad Nurdin Ibrahim Nurita Widianti nurtyas Laras Oedjijono Oedjijono Oos M. Anwas Ori T Hartonegoro Prijantono Dilyanto Pudail, M. Pudoli, Ahmad Pulung Nurtantio Purnomo , Budi Purwanto Purwanto Putri Ariatna Alia R. Prasetyo Agung Nugroho Rahmad Tullah Ramli sangaji Rani Pratikaningtyas Ratri Nugraheni Ricardus Anggi Pramunendar Rini Novrianti Sutardjo Rizanatu Fikrina Rizka Noor Miftakhul Ulum Rizki Amalia Robertus Sudaryanto Rony Kriswibowo Rosariastuti MMAR Rudi Santoso Rusina Widha Febriana S H Poromarto S. Agung S. Raharjo S. Mardin S. Mudmainah S. Suripin Safarudin Safarudin Salman Suleman Sari Widati Selfya Ningrum Setiyo Utomo Siswo Sumardiono Siti Nurchasanah Siti Nurul Azimi Slamet Minardi Slamet Prayitno Slamet Rohadi Suparto Soekma Akhriani Sri Hartati Sri Hernawati Sri Rukiyawati Sri Tutie Rahayu Sri Wahyu Agustiningsih Sri Widodo Stephanus Angger Cahyo Pratono Sudarno Sudarno Sufriadin, Sufriadin Sugiarto Sugiarto Suhaila Hasibuan Sujiono Sujiono Sukarmi Sukarmi Sumani Sumani Sunu Arsy Pratomo Suprapedi Suprapedi SUPRIYADI DARSOWIYONO Supriyadi Supriyadi Supyani Supyani Sutirman Sutirman Syafrudin Syafrudin Syahrul Donie syaifuddin syaifuddin Syamsul Huda T. Agustono T. Syahzaeni Tarjoko Tarjoko TATI NURHAYATI Utomo Utomo Virgiawan Toti Viriani Noviasari Dewi Wahyu Wahyu Wahyudi Wahyudi Wahyuningsih Wahyuningsih Yunus Indra Gunawan