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All Journal JURNAL SAINS PERTANIAN EQUATOR INFERENSI Jurnal Ilmu Ternak Veteriner AGRIVITA, Journal of Agricultural Science Al-Dzikra: Jurnal Studi Ilmu al-Quran dan al-Hadits Jurnal Biologi Tropis Jurnal Pengabdian Magister Pendidikan IPA YUME : Journal of Management E-JURNAL JUSITI : Jurnal Sistem Informasi dan Teknologi Informasi Journal Of Millennial Community Social Science Studies Jurnal Teknologi Informasi Cyberku JAKA (Jurnal Akuntansi, Keuangan dan Auditing) Unisia Jurnal Pengabdian Informatika (JUPITA) Jurnal Pendidikan dan Pengabdian Masyarakat El-Fakhru : Jurnal Ilmiah Pendidikan Islam Jurnal Kajian Penelitian Pendidikan dan Kebudayaan Jurnal Pengabdian Bersama Masyarakat Indonesia Kelimutu Journal of Community Service Jurnal Nyanadassana: Jurnal Penelitian Pendidikan, Sosial, dan Keagamaan Jurnal Mimbar: Media Intelektual Muslim Dan Bimbingan Rohani GABBAH : Jurnal Pertanian Dan Perternakan PERIODE: Jurnal Sejarah dan Pendidikan Sejarah
Abdul Syukur
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Religion Agriculture, Biological Sciences & Forestry Humanities Biochemistry, Genetics & Molecular Biology Computer Science & IT Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Environmental Science Health Professions Immunology & microbiology Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Nursing Public Health Social Sciences Veterinary Other

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PENENTUAN JURUSAN SISWA SEKOLAH MENENGAH ATAS DISESUAIKAN DENGAN MINAT SISWA MENGGUNAKAN ALGORITMA FUZZY C-MEANS Altanova Reza; Abdul Syukur; Moch Arief Soeleman
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 13 No 1 (2017): Jurnal Teknologi Informasi CyberKU Vol. 13, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Majors are held no valid high school in Indonesia is conducted when students are still in grade X. This includes the areas of interest Majors Natural Sciences, Social Sciences, and science of language. Majors will depend on the capability of student achievement in the areas of interest / courses available and in accordance with the conditions at the school. If it is not possible then the only department of particular interest are provided in school. The results of tests that tested students' interest through psychological tests aimed to help the school and the students themselves so that later, the lessons will be given to students become more focused as it has in accordance with the capability in the field of interest. Fuzzy C-Means algorithm is an algorithm that is easy and is often used in the technique of grouping the data as it makes an estimate efficient and does not require a lot of parameters. Several studies have concluded that the Fuzzy C-Means algorithm can be used to classify data based on certain attributes. In this study will be used Fuzzy C-Means algorithm to classify the student data High School (SMA) based on the value of the core subjects for the majors that are appropriated to the interests test results. The study also examined the level of accuracy of Fuzzy C-Means algorithm in determining the majors in high school. Application of Fuzzy C-Means algorithm in determining the majors in the 278 high school students were tested in this study, indicating that the FCM algorithm has a good degree of accuracy (in an average of 82.01%) by including interest test scores compared with the manual method based on the selection of individual students only 63.67%.
PEMISAHAN VOICE DAN UNVOICE MENGGUNAKAN TEKNIK OVERLAPING BLOCK, ZERO CROSSING RATE, DAN SHORT TIME ENERGY DALAM PENGENALAN SUARA Ade Yusupa; Abdul Syukur; Ricardus Anggi Pramunendar
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

Dalam proses speech recognition, speech syntesis dan speech enhancement, signal suara yang diinputkan tidak dapat langsung dikenali atau diindentifikasi sebagai gelombang signal voice atau unvoice. Proses analisis ucapan dalam menetapkan voice dan unvoice biasanya dilakukan dengan ekstrasi dari speech signal atau signal suara. Dalam penelitian ini, kami melakukan dan membandingkan 3 akurasi metode yakni: penentuan manual dengan software Adobe Audition dibandingkan pada tool matlab dengan metode separation of voice and unvoice using non overlapping block, zero-crossing rate and energy of a speech signal, dan juga membandingkan dengan metode peneliti yaitu penggabungan teknik overlapping blocks, zero crossing rate dan short time energy dalam menentukan voice dan unvoice untuk memisahkan bagian unvoice dan voice ucapan dari sinyal suara. Dengan ada perbedaan pada overlapping block dan non-overlapping block. Kami mengevaluasi hasil dari semua metode tersebut bahwa teknik yang digunakan peneliti dengan overlapping blocks, zero crossing rate dan short time energy terbukti lebih efektif dalam pemisahan voice dan unvoice.
PREDIKSI KECEPATAN ANGIN MENGGUNAKAN MODEL ARTIFICIAL NEURAL NETWORK BERBASIS ADABOOST Abdul Syukur; Catur Supriyanto; Akhmad Khanif Zyen
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

Prediction is an attempt to predict the future by examining the past. This prediction consists of the bias estimation of the magnitude of future several variables, such as sales, on the basis of knowledge of the past, present, and experience. Adaboost is one of the optimization algorithm which can improve the accuracy of a predictive value. Previous research examines the exchange rate prediction of wind speed using back propagation Artificial Neural Network algorithm. The purpose of this study is intended to improve the accuracy of prediction of wind speed previously predicted using Artificial Neural Network Backpropagation algorithm then improved the prediction accuracy using adaboost algorithm during the process of training and added back propagation Artificial Neural Network algorithm in the learning process.The results showed that the prediction accuracy of the wind speed values previously predicted using Artificial Neural Network back propagation algorithm with an accuracy of prediction error at sample time per 10 minute predictions of 0.31576596 managed to reduce the value of the accuracy of the prediction error using adaboost algorithm during training and coupled Artificial Neural Network algorithm Backpropagation learning process with an accuracy of prediction error amounting to 0.15945762.
PENENTUAN TINGKAT KESEJAHTERAAN ANAK MENGGUNAKAN ALGORITMA C 4.5 Yuli Murdianingsih; Abdul Syukur; Moch Arief Soeleman
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

Realization of child welfare is a right of every child and is the responsibility of all. Today the program services are sporadic, discontinue and responsiveness is a result of not optimal data management with social welfare problems is very large. Need a model system that can help make decisions quickly, precisely and accurately. In this research the basic needs of children based on four parameters: physical, intellectual, emotional, social and spiritual. C4.5 algorithm implementated in the m system’s model of children's basic needs level is done by calculating the entropy and the gain of the parameters of physical, intellectual, emotional and spiritual social iteratively in order to obtain a decision tree and rules used to model. Data analisys base on 149 datas as the training data and the testing data is 37. The accuracy of the model to look at the performance of the system using confusion matrix. Systems decision trees obtained the degree of basic needs of children, from the decision tree obtained seven rules that are used in view, values of accuracy obtained 94,59 % . C 4.5 algorithm can be used for classification of the level of a child's basic needs are met and not met.
PREDIKSI RENTET WAKTU JUMLAH PENUMPANG BANDARA MENGGUNAKAN ALGORITMA NEURAL NETWORK BERBASIS GENETIC ALGORITHM Mohamad Ilyas Abas; Abdul Syukur; Moch Arief Soeleman
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

Prediksi terhadap jumlah penumpang dilakukan guna memberikan informasi kepada manajamen bandar udara Djalaluddin Gorontalo. Informasi yang diberikan dapat dijadikan sebagai bahan pertimbangan dalam melakukan pengelolaan dari segi infrastrukur sarana dan prasarana dari pihak bandara. Hasil prediksi terhadap jumlah penumpang tahun mendatang akan memberikan informasi kepada pihak bandara agar dapat meningkatkan pelayanan yang lebih maksimal terhadap penumpang. Untuk itu, perlu adanya prediksi terhadap pertumbuhan jumlah penumpang salah satunya yaitu dengan penerapan salah satu algoritma dalam data mining. Penerapan algoritma Neural Network menjadi salah satu algoritma yang dapat digunakan untuk melakukan prediksi. Serta penerapan Neural Network Backpropagation sebagai proses pelatihan untuk data time series. Penambahan Algoritma Genetika untuk melakukan optimasi dapat memperkecil nilai Root Mean Squared Error (RMSE). RMSE terkecil akan menambah keakuratan dalam melakukan prediksi. Nilai RMSE yang didapat pada penelitian ini yaitu 0.092. Dengan parameter Neural Network Hidden Layer: 10, Training Cycles, 22, Learning Rate: 0.10982546098949762 dan Momentum: 0.1 serta parameter optimasi Max Generations: 50, Population Size: 50, Mutation Type: Gaussian_mutation, Selection Type: Roulette Wheel, dan Crossover Probability: 0.9. Kombinasi NN+GA ini terbukti menghasilkan RMSE terkecil untuk sehingga dapat digunakan untuk melakukan prediksi terhadap jumlah penumpang bandara di Gorontalo.
OPTIMASI PREDIKSI TINGKAT PRODUKSI BAWANG MERAH NASIONAL MENGGUNAKAN METODE BACKPROPAGATION NEURAL NETWORK BERBASIS ALGORITMA GENETIKA Fajriyanto Fajriyanto; Abdul Syukur; Catur Supriyanto
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

Bawang merah merupakan kebutuhan masyarakat yang terus meningkat seiring dengan pertambahan jumlah penduduk dan harga belinya. Oleh sebab itu, untuk mengimbangi kebutuhan agar selalu terpenuhi maka jumlah produksinya harus seimbang. Menurut Direktorat Bina Hortikultura(1980), bahwa bawang merah adalah salah satu yang memberikan preoritas utama untuk pengembangan produksi Hortikultura secara nasional. Data produksi bawang merah dari tahun 1969-2014 produksi pertahun bersifat fluktuatif disebabkan oleh meningkatnya populasi Sementara lahan yang tersedia semakin sempit. Oleh sebab itu, prediksi produksi bawang merah Nasional dibutuhkan. Metode Backpropagation merupakan metode popular untuk Teknik prediksi yang mempunyai nilai RMSE terbaik. Akan tetapi, metode Backpropagation Neural Network mempunyai beberapa kelemahan, oleh sebab itu dibutuhkan sebuah metode optimasi, salah satunya dengan metode optimasi Algoritma genetika. Penelitian ini menggunakan data produksi bawang merah Nasional yang diperoleh dari Direktorat Jendral Holtikultura untuk proses training dan testing dengan menggunakan metode Backpropagation Neural Network dan Algoritma genetika untuk optimasi input weight.Pada panelitian ini metode Backpropagation Neural Network dengan algoritma genetika sebagai optimasi inputan menghasilkan nilai RMSE 0.062 terbaik, sedangkan metode Backpropagation Neural Network tanpa optimasi algoritma genetika menghasilkan nilai RMSE 0.089.
PENGELOMPOKAN ARSIP UNIVERSITAS MENGGUNAKAN ALGORITMA K-MEANS DENGAN FEATURE SELECTION CHI SQUARE Sitti Munifah; Abdul Syukur; 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

The information in every activity is very fundamental either in technical activities or in decision making.One of those is information in the form of data records or archives. Considering the importance of therole of an archive more actively in supporting the activities, then, it needs to manage the archive betterthrough the application of information and communication technology aspect, particularly on the processof archives storage. By using electronic media in archives management, then it can give easiness instoring the archives. Related to the things discussed and due to the increasing of document numbers in thetext form which quiet large on University archives chamber, it makes the document clustering important.The document clustering is a right way and has purpose of distributing the document into some groups,which have text similarity level, term wighting and distance similarity that used at the time of archivesstoring subjectively. The objective of this writing is to clustering of archives document in the archivesstoring system, increasing of clustering document performance through term weighting TF-IDF andselection feature method. The results showed that the use of selection feature method and K-MeansAlgorithm on clustering analysis, to process the archives storing seen that there was an increasing ofaccuracy level on Manhattan Distance which previously selection feature added as 61.39% with timetaken was as 69 seconds, become 73.86% on weighting TF-IDF through selection feature of Chi Squarewith time taken needed in the process was as 9 seconds
PENERAPAN PEMBOBOTAN ATRIBUT PADA ALGORITMA NAIVE BAYES UNTUK ANALISIS SENTIMEN REVIEW APLIKASI ANDROID DARI GOOGLE PLAY Aris Tri Jaka Harjanta; Abdul Syukur; 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

Fast growing technology corelate with the demand of faster information access. Recently, the information technology dominate by android (open source) based smartphone, it makes many application developer build the application base on this operating system. With so many existing applications, users need a reference to see the application in general, although it has been provided a facility user review for this application, large number of users review are make the user difficult to be able read one by one. Thus it is necessary to know how the sentiment classification of users on the application. In this experiment, algorithms naïve bayes classifications applied are shown to have good performance on large data and have proven reliable in a variety of domains. As well as adding a attribute weighting use algoritm of weight by correlation, weight by chi squered statistical and weight by SVM on the data, so expect a good accuracy of the sentiment analysis android application to use in Indonesian sentiment.
ALGORITMA C4.5 DENGAN PARTICLE SWARM OPTIMIZATION UNTUK KLASIFIKASI LAMA MENGHAFAL AL-QURAN PADA SANTRI MAHADUL QURAN Firman Santoso; Abdul Syukur; A Zainul Fanani
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

Pondok pesantren merupakan salah satu pendidikan yang fokus terhadap bidang keagamaan. Namun, seiring berjalannya waktu pesantren di Indonesia terus berkembang sangat pesat, sudah banyak pesantren yang didalamnya sudah dilengkapi dengan ilmu umum tidak hanya agama saja. Penelitian iini bersumber dari Pondok Pesantren Salafiyah Syafi’iyah di Asrama Ma’hadul Qur’an. Dalam penelitian ini bertujuan untuk mengklasifikasi lama menghafal alqur’an dengan menggunakan algoritma C4.5 dengan Particle Swarm Optimization (PSO). Dengan menggunakan dataset lulusan santri Ma’hadul Qur’an. Dari hasil eksperiman yang dilakukan menghasilkan Decision Tree C4.5 dengan akurasi 80 %. Setelah dilakukan dengan menggunakan C4.5 dan Particle Swarm Optimization (PSO) akurasi meningkat menjadi 87 %
PENGARUH TEXT PREPROCESSING DAN KOMBINASINYA PADA PERINGKAS DOKUMEN OTOMATIS TEKS BERBAHASA INDONESIA Hadiyatun Najjichah; Abdul Syukur; Hendro Subagyo
Jurnal Teknologi Informasi - Cyberku (JTIC) Vol 15 No 1 (2019): Jurnal Teknologi Informasi CyberKU Vol. 15, no 1
Publisher : Program Pascasarjana Magister Teknik Informatika, Universitas Dian Nuswantoro

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Abstract

Numbers of information increased in concordance to the growth of them digitally. And many of informations available on the internet are in textual form. It is necessary for the information seekers to get the what they require. Automatic Text Summarization is a process of summarizing done by machine through certain methods to get a shorter form of document while still preserving the gist. This research is to examine the influence of preprocessing text and its combination to the Automatic Text Summarization of Bahasa Indonesia. Method used are segmentation, Tokenization, Stopword removal, Stemming and N-Gram. There are 14 steps of combination. The results shows that indeed there is influence of those combinations to the Automatic Text Summarization.The highest F-Measure is resulted on combinations step of Tokenization >> 2-gram >> Summarization, with 66% accuracy. While the lowest is resulted from the combination step of Tokenization >> 3-gram >> Summarization and process of Tokenization >> 4-gram >> Summarization with 63% accuracy.
Co-Authors A Zainul Fanani Aan Setiawan Abda Abda Adam Restu Ade Yusupa Adityo Putro Wicaksono Agil Al Idrus Agung Prabowo Ahmad Raksun Ahmad Raksun, Ahmad Ahmad Zainul Fanani Akhmad Khanif Zyen Alawiyah, Desi Altanova Reza Anak Agung Gede Sugianthara Annisa Zikri Robbia Arina Rahma Tika Arinal Hidayati Aris Tri Jaka Harjanta, Aris Tri Astri Aristiani Ayu Safitri Melita Ayudya Lestari Baiq Fitri Rahmayanti Baiq Indriana Sukawati Baiq Serena Diva sabatani Baiq Serena Diva Sabatini Baiq Tuluk Parawansa Rahayu Bambang Hendro Sunarminto Candra Putra Pamungkas Catur Supriyanto Daeng Mas'ud Ibrahim Desy Ika Puspitasari Didan Arya Ramadhan DIDIK SANTOSO Didik Santoso Diwahana Mutiara Candrasari Hermanto DjaÂ’far Shiddieq Dwi Alya R.R Kurnia Erlita Praha Ardila Putri Erna Martia Anggraini Fajriyanto Fajriyanto Fegi Sentiana Ferniawan, Ferniawan Fery Riyanto Firman Santoso Fisha Shalsabila Frans K. Selly Gafaruddin, Abdul Gemma Maya Gustin Ghinayatul Amjad Gustina Alfa Trisnapradika Hadiyatun Najjichah Hafizul Khatomy Hamidatul Aeni Hendro Subagyo Humaira Agustini I Wayan Merta Ine Fausayana Inggrid Tania Rinaryanto Jamaluddin JAMALUDIN Jami’atul Aulia Junia Efriana Karnan Karnan Khairuddin - Khairudin Khairudin Laela Innaza Laela Salsa A Lakui Johary Lalu Japa Lalu Muh. Sofyan Hadinata Lalu Raftha Patech Lalu Zulkifli Lalu Zulkifli Lina Kurniatun M. Fahmi Khairul Umam M. Tufaila M. Yamin Mahrus Mahrus Mahrus Makleat, Nirwaning Mariana Ikun RD Pareira Mario Rere Garcia Djaro Masita Mattalatta Mattalatta Miftahul Khairat Mila Mentari Moch Arief Soeleman Mohamad Ilyas Abas Mohammad Liwa Ilhamdi Muh Arafatir Al Jarwi Muhamad Bisri Mustofa Muhammad Danial Ali Yafi Muhammad Khalid Rozani Muhammad Yamin Nanang Riadi Nina Nurraudatul Jannah Nisrina Nisrina Noffiyanti Norrodzoh Binti Hj Siren Nugrohotomo Nugrohotomo Nuraeni S Nurhudayati Nurjani Nurlaili Agustin Nurul Hatika Oktaviana Wirnis Agrian Fani Prapto Yudono Raudatul Jannah Rian Aprianto Ricardus Anggi Pramunendar Rifqi Aulia Erlangga Rika Damayanti Rizky Jasahuldia Sajili, Lalu Alvin Maulana Salsa Dela Asmarini Samtika , Nuning Saraswati, Galuh Sarwan Ardani Setia Budi Shonhaji Shonhaji Sigit Wibowo Siti Wuryan Sitti Munifah Soemitro Padmowijoto Sofyan Hidayat Sri Tejowulan Stofani S. Lima suhendi suhendi Sulakhudin Sulakhudin Suprapedi Suprapedi Suwardji Syachruddin Tania Fadillah Tiara Karantina Tita Syahru Ramdhani Umasih Umasih Umasih Wahyu Saputra X Zardht Alex Hidayat Yadi Sumyadi Yeni Susanti Yohannes Panca Mukti Yosephina K. Sogen Yudira Adhani Yuli Murdianingsih Yuni Kurniasi Zaenal Bachruddin Zelisa Nudia Fitri Zoul Fakar Zulhalifah Zulhalifah