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All Journal Semantik Jurnal Pseudocode Wahana Teknik Sipil: Jurnal Pengembangan Teknik Sipil BANGUN REKAPRIMA Jurnal Teknologi Informasi Cyberku Jurnal Hilirisasi Technology kepada Masyarakat (SITECHMAS) Journal of Computing Theories and Applications Journal of Future Artificial Intelligence and Technologies
Stefanus Santosa
Politeknik Negeri Semarang
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Social Sciences Transportation Other

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Journal : Jurnal Teknologi Informasi Cyberku

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MODEL KLASTERISASI GENRE CERPEN KOMPAS MENGGUNAKAN K-MEANS Hario Guritno; Stefanus Santosa
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

Information in the form of a text document can be found at any time on print media. Every time the community is faced with the current wave of information like the arrival in the form of unstructured text documents and have penetrated our lives and culture. Unstructured information comes closer all the entities of the world community. The mass media published the newspaper every day is the biggest contributor to human relations around the world. KOMPAS newspaper published every Sunday always insert the rubric of short stories in it. There is a problem to distinguish the genre of stories with one another. This research proposed a model of classify KOMPAS short stories with K-Means algorithm to get the solution. Accuracy of this proposed model using the Davies Bouldin Index (DBI) is 0.001.
ANALISIS KERANJANG PASAR UNTUK REKOMENDASI PRODUK (CONSUMER GOOD) MENGGUNAKAN FP-GROWTH DENGAN KLASTERISASI CLARANS Stefanus Santosa; Jadi .
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

Market basket analysis is a generic term for methodology that study the composition of a basket of products. It has the objective of indentifying products, or groups of products, that tend to occur together (are associated). The discovery of this relationship can help merchant to develop a strategy of sales to consider the goods are often purchased with by customer. The knowledge that obtained market analysis basket is very important, because it can help recommendations product and promotion products so marketing strategy to be more appropriate. Market basket analysis can approach with Association Rule, such as apriori and FP-Growth. But they are a number of technical issues relating to the most common recommendations techniques. Association Rule tend to ignore the large itemset, To overcome these problems, existing attributes clustered to form groups of the same attributes and then determine the association patterns in each group. This study will use CLARANS algorithm for clustering on sales data and apply the FP-Growth algorithm to approach the association in each cluster. So that the product recommendations to customers to be more accurate because the Dataset that will be associated to be smaller. To the experimentally determined value of Minimum Support is 70% - 100% and Confidence Minimum value 70% - 100%. From the measurement results using Support, Confidence and Lift Ratio isfound that a high number of rule in third cluster.
PENERAPAN LEARNING TECHNOLOGY SYSTEM ARCHITECTURE (LTSA) PADA MULTIMEDIA PEMBELAJARAN PERAKITAN PC Stefanus Santosa; April Firman Daru
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

Conventional learning systems still dominate the learning process at various universities. In general, learning in college lecture reflects patterns that tend in the same direction. Students the opportunity to conduct personal understanding through looping and less enrichment accommodated properly. This research tried to solved the problem. Hence, it is important to develop a teaching medium based on Learning Technology Systems Architecture (LTSA) with multimedia tutorial approach. From the test results of learning can be stated that the learning method PC assembly using multimedia-based teaching tools can support the learning that is interactive, engaging, efficient, effective, and meaningful. In addition test results also showed a significant difference compared to conventional teaching methods . Students using conventional learning systems only obtained an average score of 49.6, while students use learning system using learning tools of animation and visualization obtain an average value of 80 , 09. This suggests that the use of teaching aids by using multimedia (text, audio, video and animation) more easily understood by the students so deserves its place as a major strategy in the laboratory learning.
PEMODELAN DESAIN CAMPURAN BETON DENGAN BACKPROPAGATION NEURAL NETWORKS Stefanus Santosa; Basuki Setiyo Budi; Karnawan Joko Setiyono; Tjokro Hadi; Triatmo Sugih Hardono
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

Concrete is a mixture of materials that has complex characteristic so that it raises a variety of very complex models as well. The experts in concrete mixing believe that the formula to find compressive strength of a mixture is not good enough. Every mixture design only applies to one mixture only. Because of that, every mixture production who need even the slightiest diferrences in the base materials, will need a new mixture design. Concrete mixture modeling process is done manually with a variety of mixed composition and destructively testing has some drawbacks like expensive, unpredictable, and not environmental friendly. Besides of that, state of the art concrete mixture design modelling computation with Multilayer Perceptron Artificial Neural Network s (MLP) have RMSE = 5,27. Computational model developed in this study with the same data sets has more good performace than MLP model. From the results of experiments that have been carried out proved that the proposed model, Backpropagation Neural Network (BPNN), has lower error rate than MLP with RMSE = 4.18.
TEXT MINING UNTUK KLASIFIKASI PENGADUAN PADA SISTEM LAPOR MENGGUNAKAN METODE C4.5 BERBASIS FORWARD SELECTION Ali Sofyan; Stefanus Santosa
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

Report of public complaints on the site becomes a medium of communication between the community and government agencies. The number of incoming documents every day be a source of information to measure the services of government agencies. Classification of documents is very important to do otherthan to ensure that the intended objectives of the institution, as well as to classify complaints fit the category. C4.5 algorithm is one of the algorithms that can be used for classification. There were some complaints classification research. This study aims to apply the classification of complaints by algritma C4.5 with a selection of features to improve the accuracy of classification. Results of experiments with methods of research division of the number of datasets, cross validation, classification with and without features. The test results obtained by testing the value of the best accuracy with 550 documents with forward selection, with cross valiadtion 9folds with a value of 85.27%. precission 87.8% and 85.3% recall
MODEL PREDIKSI POLA LOYALITAS PELANGGAN TELEKOMUNIKASI MENGGUNAKAN NAIVE BAYES DENGAN OPTIMASI PARTICLE SWARM OPTIMIZATION Stefanus Santosa; Roy Yuliantara
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

Tantangan yang dihadapi dalam penerapan CRM di perusahaan operator telekomunikasi seluler adalah usaha menurunkan jumlah pelanggan yang berhenti menggunakan layanan perusahaan dan kemungkinan pindah ke perusahaan kompetitor (Churn). Penelitian untuk mencari solusi atas masalah tersebut dapat dilakukan melalui data mining, Dari beberapa penelitian pada konstalasi penelitian tentang Model Prediksi Loyalitas Pelanggan Telekomunikasi menunjukkan hasil yang baik. State of The Art dari konstalasi ini adalah ditemukannya Model Prediksi Loyalitas Pelanggan Telekomunikasi menggunakan algoritma Backpropagation dengan seleksi fitur PSO dengan nilai akurasi sebesar 85,48%. Hasil akurasi yang didapatkan dirasa kurang maksimal, maka penelitian ini mencoba memperbaiki akurasi model prediksi dengan menggunakan algoritma Naïve Bayes berbasis Particle Swarm Optimization. Model Prediksi Loyalitas Pelanggan Telekomunikasi yang diusulkan dalam penelitian ini menunjukkan hasil yang baik. Diperoleh nilai akurasi yang lebih tinggi daripada penelitian sebelumnya, yakni nilai accuracy adalah 98,54 % dan nilai AUC adalah 0,99.
KLASTERISASI GENRE CERPEN KOMPAS MENGGUNAKAN AGGLOMERATIVE HIERARCHICAL CLUSTERING- SINGLE LINKAGE Zenal Arifin; Stefanus Santosa; M. 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

Teks merupakan sarana interaksi dalam semua media komunikasi tulisan. Oleh karena peningkatan ukuran dan jenisnya yang sangat cepat, maka proses analisis data teks menjadi sesuatu yang bermakna sangatlah penting. Penggalian teks telah menjadi teknologi yang penting terutama dalam pengolahan dokumen cerpen. Pembaca cerpen saat ini kesulitan untuk memperoleh cerpen yang diinginkan jika cerpen tersebut tidak terkelompok dengan baik. Jika pengelompokan dilakukan secara manual membutuhkan waktu yang sangat lama. Oleh sebab itu, clustering menjadi solusi untuk mengatasi masalah tersebut. Clustering cerpen berfungsi untuk mengelompokkan dokumen cerpen berdasarkan tingkat kemiripan dari dokumen cerpen tersebut. Penelitian ini mengusulkan suatu model klasterisasi berbasis metode Hierarchical Clustering, khususnya Single Linkage Clustering. Metode Hierarchical Aggomerative Clustering terbukti memiliki performansi yang lebih baik daripada pendekatan penelitian sebelumnya yang menggunakan k-Means. Dari 127 dataset cerpen yang telah diujicobakan didapatkan nilai akurasi dari metode Agglomerative Hierarchical Clustering Single Linkage 47,2441 %, sedangkan metode k-Means hanya 37,7953 %.
KLASIFIKASI PESAN SMS MENGGUNAKAN ALGORITMA NAIVE BAYES DENGAN SELEKSI FITUR GENETIC ALGORITHM Indah Munitasri; Stefanus Santosa; Catur Supriyanto
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

Short Message service (SMS)is mobile communication that interest advertiser for its effective deliveries with cheap operational cost compare to printed media. Some spam SMS do not need mailing list to reach their customers. But, spam SMS could create higher respons from emails spam. Spam SMS includes promotion,scamming,and fraud.To overcome this problem,anti-spam filtering are needed to detect spam and non-spam SMS. Some anti-spam filtering algoritm such as Decission Tree, Naïve Bayes (NB),Support Vector Machine (SVM),and Neural Network. This research used Naïve Bayes classifier or known as multinominal Naïve Bayes is a simplification from Bayes algoritm which is suitable for text or documents classification.This study will make additional Genetic Algorithms in the process of selecting attributes that will be used in the classification process with Naïve Bayes algorithm. Genetic Algorithms can be used as an attribute of the overall voter attributes obtained from the process of feature extraction. NB compared to NB and GA produced significant accuracy result, NB gained 89.39% accuracy rate, but GA gained 89.73% accuracy rate. So, there is an increase in 0.34 % after adding GA. NB and GA can be applied to the classification of SMS messages, because Naïve Bayes algorithm is an algorithm that does not consider the relationship between attributes to one another (independence). So, when there is a data set with hundreds of attributes, all of those attributes will be counted by Naïve Bayes, by adding a Genetic Algorithm as a feature selection, which determines the attributes that are relevant in order to optimize the classification accuracy. It is expected to apply feature selection using Particle Swarm Optimization (PSO) for the next research because there is no evolution in the operator, for example, mutation and crossover on Genetic Algorithms (GA,) and PSO is more flexible in maintaining the balance between global and local searches on its search space.
KLASIFIKASI CITRA TELUR FERTIL DAN INFERTIL DENGAN ANALISIS TEKSTUR GRAY LEVEL CO-OCCURRENCE MATRIX DAN SUPPORT VECTOR MACHINE Dewi Nurdiyah; Stefanus Santosa; Ricardus Anggi Pramunendar
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

Fertility eggs test are steps that must be performed in an attempt to hatch eggs. Fertility testusually use egg candling. The purpose of observation is to choose eggs fertile (eggs containedembryos) and infertile eggs (eggs that are no embryos). And then fertilized egg will be entered intothe incubator for hatching eggs and infertile can be egg consumption. However, there are obstaclesin the process of sorting the eggs are less time efficient and inaccuracies of human vision todistinguish between fertile and infertile eggs. To overcome this problem, it can be used ComputerVision technology is having such a principle of human vision. It used to identify an object basedon certain characteristics, so that the object can be classified. The aim of this study to classifyimage fertile and infertile eggs with SVM (Support Vector Machine) algorithm based on inputfrom bloodspot texture analysis and blood vessels with GLCM (Gray Level Co-occurrenceMatrix). Eggs image studied are 6 day old eggs. It is expected that the proposed method is anappropriate method for classification image fertile and infertile eggs.
OPTIMASI PARAMETER ARTIFICIAL NEURAL NETWORK DENGAN MENGGUNAKAN ALGORITMA GENETIKA UNTUK MEMPREDIKSI NILAI TUKAR RUPIAH Khairul Fahmi; Stefanus Santosa; Ahmad Zainul Fanani
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

To predict foreign exchange rate is not easy, accurate prediction is necessary for investor to reduce higrisk about exchange rate volatility. In predicting foreign exchange rate is used Artificial Neural NetworkBackpropagation as a model that applied. There are several parameters to implement Artificial NeuralNetwork that must be determined as training cyclel, learning rate, and momentum, the problem is the lackof standard guidelines in determining the parameters that will be used, therefore in this method used theexperimental method. So that we need a method that can resolve the problem, then that the parametersobtained become more optimal. Solutions that can be applied is to apply the genetic algorithm (GA) onArtificial Neural Networks, in order to optimize the value of training cycle, learning rate and momentumparameters. The results are the application of optimization techniques with Genetic Algorithm canfacilitate the search for optimal parameter values and reduce error (RMSE) or increase the value of theaccuracy of the Artificial Neural Network algorithm, thus the model obtained can be used by investor topredict foreign exchange rate.
Co-Authors Abd. Rasyid Syamsuri Adityawan, Harish Trio Agus Setyawan Agus Widjanarko Ahmad Zainul Fanani Ajib Susanto Ali Sofyan Anung Suwarno, Anung April Firman Daru Basuki Setiyo Budi BASUKI SETIYO BUDI S.T., M.T. Catur Supriyanto Catur Supriyanto Catur Supriyanto Supriyanto De Rosal Ignatius Moses Setiadi Dewi Nurdiyah Dianita Ratna Kusumastuti Edi Noersasongko Erni Rahmawatie Fahdiyat, Lukman Fahdiyat, Lukman Farroq, Omar Fatkhuroji Fatkhuroji Fenilinas Adi Artanto Gan, Hong-Seng Goro, Garup Lambang Hadi Wibowo Hadi, Tjokro Hario Guritno Heri Triluqman Budisantoso Ilala, Oze Dora Indah Munitasri Islam, Hussain Md Mehedul Isnubroto, Danang Jadi . Joko, Karnawan JUNAIDI S.T., M.Eng. Karnawan Joko Setiyono Khairul Fahmi Leily Fatmawati, Leily M. Arief Soeleman Marchus Budi Utomo Marchus Budi Utomo, Marchus Budi MARSUDI Marsudi Marsudi Martono Martono Martono Martono Martono Martono Mawardi Mawardi Mochammad Tri Rochadi Nur Aeni Widiastuti Ojugo, Arnold Adimabua Pertiwi, Zulaikha Putri Pertiwi, Zulaikha Putri Praharseno, Fikri Pratama, M Hafidh Aditya Putra, Erwin Dwika Rabinah, Aiun Hayatu Ricardus Anggi Pramunendar Rifqi Aulia Abdillah, Rifqi Aulia Roselina Rahmawati Roy Yuliantara S, Sri Wahyuningsih Sarker, Md Kamruzzaman Setiyono, Karnawan Joko Setyaningsih, Desi SUDARMONO SUDARMONO Suhartono, Edy Sukoyo Sukoyo Sulaiman, Sri Wahyuningsih Sulaiman, Sriwahyuningsih Supriyadi Supriyadi Supriyo Supriyo Supriyo Suroso Suroso Suroso Suroso Suwarto Suwarto Suwarto Suwarto Tjokro Hadi TJOKRO HADI SST., M.T. Triatmo Sugih Hardono W, Herry Ludiro Wahyono, Herry Ludiro Wicaksono, M Rafi Wiji Lestari Yonathan Purbo Santosa Yudha Tirto Pramonoaji Yusetyowati Yusetyowati, Yusetyowati Zenal Arifin Zuama, Leygian Reyhan