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All Journal ComEngApp : Computer Engineering and Applications Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Jurnal Buana Informatika Perfecting a Video Game with Game Metrics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Teknik Elektro Dinamika Informatika Jurnal Ilmiah Kursor Proceeding of the Electrical Engineering Computer Science and Informatics Journal of Engineering and Technological Sciences Jurnal Edukasi dan Penelitian Informatika (JEPIN) jurnal Teknologi Informasi Magister Proceeding of the Electrical Engineering Computer Science and Informatics Edu Komputika Journal Communications in Science and Technology Conference SENATIK STT Adisutjipto Yogyakarta Jurnal Otomasi Kontrol dan Instrumentasi IJID (International Journal on Informatics for Development) Jurnal Informatika IJITEE (International Journal of Information Technology and Electrical Engineering) Indonesian Journal of Electrical Engineering and Computer Science Jurnal Nasional Teknik Elektro dan Teknologi Informasi Prosiding Seminar Nasional Rekayasa Teknologi Industri dan Informasi ReTII
Teguh Bharata Adji
Departemen Teknik Elektro Dan Teknologi Informasi Fakultas Teknik, Universitas Gadjah Mada
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering Physics Transportation

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Impact of Matrix Factorization and Regularization Hyperparameter on a Recommender System for Movies Fathan, Gess; Bharata Adji, Teguh; Ferdiana, Ridi
Proceeding of the Electrical Engineering Computer Science and Informatics Vol 5: EECSI 2018
Publisher : IAES Indonesia Section

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (336.245 KB) | DOI: 10.11591/eecsi.v5.1685

Abstract

Recommendation system is developed to match consumers with product to meet their variety of special needs and tastes in order to enhance user satisfaction and loyalty. The popularity of personalized recommendation system has been increased in recent years and applied in several areas include movies, songs, books, news, friend recommendations on social media, travel products, and other products in general. Collaborative Filtering methods are widely used in recommendation systems. The collaborative filtering method is divided into neighborhood-based and model-based. In this study, we are implementing matrix factorization which is part of model-based that learns latent factor for each user and item and uses them to make rating predictions. The method will be trained using stochastic gradient descent with additional tricks and optimization of regularization hyperparameter. In the end, neighborhood-based collaborative filtering and matrix factorization with different values of regularization hyperparameter will be compared. Our result shows that matrix factorization method with lowest regularization hyperparameter outperformed the other methods in term of RMSE score. In this study, the used functions are available from Graphlab and using Movielens 100k data set for building the recommendation systems.
Texture Analysis for Skin Classification in Pornography Content Filtering Based on Support Vector Machine Nugroho, Hanung Adi; Rahadian, Fauziazzuhry; Adji, Teguh Bharata; Buana, Ratna Lestari Budiani
Journal of Engineering and Technological Sciences Vol 48, No 5 (2016)
Publisher : ITB Journal Publisher, LPPM ITB

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (203.203 KB) | DOI: 10.5614/j.eng.technol.sci.2016.48.5.6

Abstract

Nowadays, the Internet is one of the most important things in a human?s life. The unlimited access to information has the potential for people to gather any data related to their needs. However, this sophisticated technology also bears a bad side, for instance negative content information. Negative content can come in the form of images that contain pornography. This paper presents the development of a skin classification scheme as part of a negative content filtering system. The data are trained by grey-level co-occurrence matrices (GLCM) texture features and then used to classify skin color by support vector machine (SVM). The tests on skin classification in the skin and non-skin categories achieved an accuracy of 100% and 97.03%, respectively. These results indicate that the proposed scheme has potential to be implemented as part of a negative content filtering system.
Perbandingan Performa Relational, Document-Oriented dan Graph Database Pada Struktur Data Directed Acyclic Graph Setialana, Pradana; Adji, Teguh Bharata; Ardiyanto, Igi
Jurnal Buana Informatika Vol 8, No 2 (2017): Jurnal Buana Informatika Volume 8 Nomor 2 April 2017
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jbi.v8i2.1079

Abstract

Abstract.Directed Acyclic Graph (DAG) is a directed graph which is not cyclic and is usually employed in social network and data genealogy. Based on the characteristic of DAG data, a suitable database type should be evaluated and then chosen as a platform. A performance comparison among relational database (PostgreSQL), document-oriented database (MongoDB), and graph database (Neo4j) on a DAG dataset are then conducted to get the appropriate database type. The performance test is done on Node.js running on Windows 10 and uses the dataset that has 3910 nodes in single write synchronous (SWS) and single read (SR). The access performance of PostgreSQL is 0.64ms on SWS and 0.32ms on SR, MongoDB is 0.64ms on SWS and 4.59ms on SR, and Neo4j is 9.92ms on SWS and 8.92ms on SR. Hence, relational database (PostgreSQL) has better performance in the operation of SWS and SR than document-oriented database (MongoDB) and graph database (Neo4j).Keywords: database performance, directed acyclic graph, relational database, document-oriented database, graph database Abstrak.Directed Acyclic Graph (DAG) adalah graf berarah tanpa putaran yang dapat ditemui pada data jejaring sosial dan silsilah keluarga. Setiap jenis database memiliki performa yang berbeda sesuai dengan struktur data yang ditangani. Oleh karena itu perlu diketahui database yang tepat khususnya untuk data DAG. Tujuan penelitian ini adalah membandingkan performa dari relational database (PostgreSQL), document-oriented database (MongoDB) dan graph database (Neo4j) pada data DAG. Metode yang dilakukan adalah mengimplentasi dataset yang memiliki 3910 node dalam operasi single write synchronous (SWS) dan single read (SR) pada setiap database menggunakan Node.js dalam Windows 10. Hasil pengujian performa PostgreSQL dalam operasi SWS sebesar 0.64ms dan SR sebesar 0.32ms, performa MongoDB pada SWS sebesar 0.64ms dan SR sebesar 4.59ms sedangkan performa Neo4j pada operasi SWS sebesar 9.92ms dan SR sebesar 8.92ms. Hasil penelitian menunjukan bahwa relational database (PostgreSQL) memiliki performa terbaik dalam operasi SWS dan SR dibandingkan document-oriented database (MongoDB) dan graph database (Neo4j).Kata Kunci: performa database, directed acyclic graph, relational database, document-oriented database, graph database
ANALISIS SENTIMEN DATA PRESIDEN JOKOWI DENGAN PREPROCESSING NORMALISASI DAN STEMMING MENGGUNAKAN METODE NAIVE BAYES DAN SVM Saputra, Nurirwan; Adji, Teguh Bharata; Permanasari, Adhistya Erna
Dinamika Informatika Vol 5, No 1 (2015): Jurnal Dinamika Informatika
Publisher : Universitas PGRI Yogyakarta

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Abstract

Jokowi merupakan seorang tokoh masyarakat dengan jenjang karir yang sangat cepat, dan tidak luput dari pandangan masyarakat baik itu positif, netral maupun negatif. Data mengenai Jokowi yang berisikan komentar positif , netral dan negatif yang berasal dari media sosial dan blog politik diperlukan dalam menentukan langkah-langkah yang harus diambil oleh Jokowi untuk mendapatkan kepercayaan dari masyarakat. Selain itu data yang sudah didapat perlu dievaluasi untuk menunjukkan urgensi diimplementasikannya preProcessing terhadap data, yaitu normalisasi dan stemming. Analisis sentimen merupakan ilmu yang berguna untuk menganalisis pendapat seseorang, sentiment seseorang, evaluasi seseorang, sikap seseorang dan emosi seseorang ke dalam bahasa tertulis. Penelitian ini menggunakan search techniques dalam pengambilan data, sehingga pengambilan data dilakukan dengan efektif dan efisien. Search techniques dalam penelitian ini menggunakan Boolean searching dengan operator “AND”. Data yang sudah didapat dilabeli positif, netral dan negatif oleh penulis kemudian dikoreksi oleh ahli bahasa. Setelah itu dilakukan preProcessing baik itu mengubah kata tidak baku menjadi baku atau biasa disebut normalisasi menggunakan kamus dan mencari akar kata yaitu stemming dengan bantuan aplikasi Sastrawi Master. Selanjutnya dilakukan juga tokenisasi N-Gram, Unigram, Bigram, dan Trigram terhadap kalimat,  kemudian menghilangkan kata-kata yang umum digunakan dan tidak mempunyai Informasi yang berharga pada suatu konteks atau biasa disebut stopword removal, dan mempertahankan emoticon karena emoticon merupakan simbol yang menunjukkan ekspresi seseorang ke dalam tulisan. Akurasi yang terbaik dalam penelitian ini adalah dengan dilakukan normalisasi dan stemming pada data sebesar 89,2655% menggunakan metode SVM, dan kemudian data yang dinormalisasi saja sebesar 88,7006% menggunakan metode SVM. Dalam penelitian ini, tidak ada ujicoba terhadap data yang dilakukan stemming saja, dikarenakan tahap yang harus dilakukan dalam stemming adalah melakukan normalisasi terlebih dahulu terhadap data.
The Social Engagement to Agricultural Issues using Social Network Analysis Widiyanti, Tanty Yanuar; Adji, Teguh Bharata; Hidayah, Indriana
IJID (International Journal on Informatics for Development) Online First
Publisher : Universitas Islam Negeri (UIN) Sunan Kalijaga Yogyakarta

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Abstract

The use of social media as a means of retrieving data because it is considered broader, straightforward, honest, efficient and real time. Not only as an expanding network, social media is also used by most people to actively participate in voicing opinions, getting assumptions, seeing current trends, and even as a forum for obtaining detailed and accurate information. Twitter is one of the social media that is microblogging, and emphasizes the speed of communication. In this era of 4.0, the government also promotes the distribution of information through social media, because it is considered to be more included in the community from various lines. The Ministry of Agriculture is involved in the use of social media as a forum for the socialization of work programs and achievements, as well as public information openly. In previous research, Social Network Analysis was used to see the relationship between actors in a work environment, or as a basis for identification in decision making on the application of technology adoption, whereas no one has used SNA to see trends in people's response to agricultural information. This study aims to see the extent to which information about agriculture reaches the community, as well as to see the community's response to take part in agricultural development. Besides that, this article will also show the actors who took part in disseminating information. Data was taken on November 13 to 20, 2020 from the Drone Emprit Academic, and was taken to be limited to 3000 nodes, then the results of the Social Network Analysis then calculated the values of Degree Centrality, Betweenness Centrality, Closeness Centrality, and Eigenvector Centrality. @AdrianiLaksmi has the highest value in Eigenvector Centrality and Degree Centrality, he has the greatest role in disseminating information and the most connections (followers) among other accounts that spread the same information.   while the @RamliRizal account ranks the highest in Betweenness Centrality, who has the most frequently referred to information that has been circulating, and the highest Closeness Centrality is owned by the @baigmac account is the fastest person to retweet the first information written.
EVALUASI METODE LOAD BALANCING Dan FAULT TOLERANCE PADA SISTEM DATABASE SERVER APLIKASI CHAT Hamka, Cakra Aminuddin; Adji, Teguh Bharata; Sulistyo, Selo
Edu Komputika Journal Vol 5 No 1 (2018): Edu Komputika Journal
Publisher : Jurusan Teknik Elektro Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukomputika.v5i1.23029

Abstract

Abstract The development of social network applications has growth so fast with various applications supported by smart tools as multiplatform to used it. Several applications that used by user are the chat and media social app. However, there is problem is the number of users accessing the chat service is large. This condition makes communication into database server can be stop and loss of data. It is caused by excessive load received by a single server database. Therefore, this study designs a database server to solve a lot of users of communication server and storage capacity into database server more than one. This is important because to increase availibiliy of services for each users request. This study used a method of distribution of communications service request for each database server. Distribution of communication service request used a load balancing method and HAPRoxy with scheduling method. There are two algorithms in scheduling method are round robin and least connection algorithm. Both of algorithms were evaluated. compared, and used in database server more than one. The result shown average of least connection algorithm has value of response time 32.421 ms is smaller than round robin algorithm 35.813 ms. While on the throughput, least algorithm has big value 211.267 Kb/s than round robin algorithm has value 210.298 Kb/s. The result shown the number of packet least connection algorithm are big and better than round robin algortihm with load balancing implementation for distribution of server communication on database server more than one able to solve a lot of services communication. Keywords: load balancing, database server chat, least connection, round robin, response time, throughput
Rekomendasi Berdasarkan Nilai Pretest Mahasiswa Menggunakan Metode Collaborative Filtering dan Bayesian Ranking Stefani, Brillian; Adji, Teguh Bharata; Kusumawardani, Sri Suning; Hidayah, Indriana
Edu Komputika Journal Vol 5 No 1 (2018): Edu Komputika Journal
Publisher : Jurusan Teknik Elektro Universitas Negeri Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.15294/edukomputika.v5i1.23077

Abstract

Abstract- Self-Regulated Learning (SRL) skill can be improved by improving students’ cognitive and metacognitive abilities. To improve metacognitive abilities, metacognitive support in learning process using e-learning needs to be included. One of the example is assisting students by giving feedbacks once students had finished doing specific avtivities. The purpose of this study was to develop a pedagogical agent with the abilities to give students feedbacks, particularly recommendations of lesson sub-materials order. Recommendations were given by considering students pretest scores (students’ prior knowledge). The computations for recommendations used Collaborative Filtering and Bayesian Ranking methods. Results obtained in this study show that based on MAP (Mean Average Precision) testings, Item-based method got the highest MAP score, which was 1. Computation time for each method was calculated to find runtime complexity of each method. The results of computation time show that Bayesian Ranking had the shortest computation time with 0,002 seconds, followed by Item-based with 0,006 seconds, User Based with 0,226 seconds, while Hybrid has the longest computation time with 0,236 seconds. Keyword- self-regulated learning, metacognitive, metacognitive support, feedback, pretest (prior knowledge), Collaborative Filtering, Bayesian Ranking, Mean Average Precision, runtime complexity.
Stemming Influence on Similarity Detection of Abstract Written in Indonesia Tari Mardiana; Teguh Bharata Adji; Indriana Hidayah
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 14, No 1: March 2016
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v14i1.1926

Abstract

In this paper we would like to discuss about stemming effect by using Nazief and Adriani algorithm against similarity detection result of Indonesian written abstract. The contents of the publication abstract similarity detection can be used as an early indication of whether or not the act of plagiarism in a writing. Mostly in processing the text adding a pre-process, one of it which is called a stemming by changing the word into the root word in order to maximize the searching process. The result of stemming process will be changed as a certain word n-gram set then applied an analysis of similarity using Fingerprint Matching to perform similarity matching between text. Based on the F1-score which used to balance the precision and recall number, the detection that implements stemming and stopword removal has a better result in detecting similarity between the text with an average is 42%. It is higher comparing to the similarity detection by using only stemming process (31%) or the one that was done without involving the text pre-process (34%) while applying the bigram.
Analisis Perbandingan Komputasi GPU dengan CUDA dan Komputasi CPU untuk Image dan Video Processing Bagus Kurniawan; Teguh Bharata Adji; Noor Akhmad Setiawan
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2015
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Teknologi yang semakin maju memicu berbagaiparadigma komputasi untuk terus berkembang, tidak terkecualimengenai teknik image processing maupun video processing yangdibutuhkan masyarakat untuk memanipulasi gambar gunakebutuhan informasi. Komputasi Graphics Processing Unit(GPU) menjadi salah satu alternatif komputasi paralel yangmenawarkan kinerja komputer yang lebih cepat daripadakomputasi Central Processing Unit (CPU) dengan memanfaatkankartu grafis. Penelitian ini menganalisis teknik komputasi paralelGPU dengan Compute Unified Device Architecture (CUDA) danmembandingkan hasil kinerja dari komputasi sekuensial CPUdengan OpenCV yang dianalisis menggunakan metodeeksperimen. Eksperimen dilakukan dengan implementasi imagedan video processing untuk operasi grayscale, negatif, dan deteksitepi. Penelitian ini menunjukkan sebuah hasil bahwa imageprocessing untuk operasi grayscale dan negatif, komputasiparalel GPU lebih unggul antara 0.2 hingga 2 detik. Sedangkanuntuk operasi deteksi tepi, komputasi GPU unggul hingga 14detik. Atau 2.8 kali lipat lebih cepat daripada komputasi CPU.Untuk video processing, komputasi CPU lebih unggul darikomputasi GPU selisih antara 1-2 frame per second.
Appropriate Data mining Technique and Algorithm for Using in Analysis of Customer Relationship Management (CRM) in Bank Industry Maghfirah Maghfirah Maghfirah; Teguh Bharata Adji; Noor Akhmad Setiawan
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2015
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Abstract—Customer Relationship Management (CRM)adalah ide yang menjadi sebuah peningkatan kepentingan faktorsukses untuk bisnis ke depannya. CRM adalah proses darimengatur interaksi antara sebuah perusahaan danpelanggannya. Pada awalnya, ini termasuk ke dalam segmentasipasar untuk mengidentifikasi pelanggan dengan potensial profityang tinggi, dari strategi pemasaran yang dirancang dengan baikuntuk mempengaruhi tingkah laku dari pelanggan dalam segmentersebut. Dalam masyarakat modern, pelanggan menjadi asetyang penting bagi perusahaan. Hubungan antara pelanggandengan manajemen yang efisien adalah metode yang dibutuhkanuntuk meningkatkan keuntungan lebih dari perusahaan.Termasuk di industri perbankan, misalnya, di sebuahperusahaan industri perbankan digunakan konsep CRMkhususnya dengan menggunakan salah satu model strategipemasaran yaitu Customer Segmentation yang bertujuan untukmembantu pihak bank untuk membagi pasar menjadi kelompoknasabah yang terbedakan dengan kebutuhan, karakteristik atautingkah laku yang berbada yang mungkin membutuhkan produkatau bauran pemasaran yang terpisah. Customer Segmentationdapat dilakukan dengan bantuan teknik Data Mining, sehinggadiharapkan dapat dihasilkan Customer Segmentation yang sesuaidengan kebutuhan bank yang dapat meningkatkan kualitasservis dan revenue dari bank tersebut.Penerapan data mining untuk sistem CRM diperbankan seharusnya menggunakan teknik dan algoritma yangtepat. Untuk itu, paper ini akan membahas mengenai bagaimanacara untuk menentukan teknik dan algoritma data mining yangtepat untuk sistem CRM di perbankan.Keywords—Customer Relationship Management (CRM);Data Mining; Bank Customer Segmentation
Co-Authors Abdul Kadir Adhistya Erna Permanasari Adi Wijaya Adi Wijaya Aris Budianto, Aris Bagus Kurniawan Buana, Ratna Lestari Budiani Dwi Retno Puspita Sari Dyah Mustika, Dyah Faisal Najamuddin Fathan, Gess Fauziazzuhry Rahadian Gess Fathan Hairani Hairani Hamka, Cakra Aminuddin Hamka, Cakra Aminuddin Hanung Adi Nugroho Hanung Adi Nugroho Hanung Adi Nugroho Hesti Khuzaimah Nurul Yusufiyah I Made Artha Agastya Igi Ardiyanto Ignatia Dhian Estu Karisma Ratri Indriana Hidayah KZ Widhia Oktoeberza Maghfirah Maghfirah Maghfirah Maghfirah Maghfirah Moch. Ari Nasichuddin Muhammad Esa Permana Putra Naufal Azmi Verdikha Nisrina Akbar Rizky Putri Noor Akhmad Setiawan Nugroho, Arseto Satriyo Nur Annisa Tresnasari Nurirwan Saputra, Nurirwan Rahadian, Fauziazzuhry Ratna Lestari Budiani Buana Ridi Ferdiana Risanuri Hidayat Rudy Hartanto Sampurna Dadi Riskiono Selo Sulistyo, Selo Setialana, Pradana SRI RAHAYU Sri Suning Kusumawardani Stefani, Brillian Stefani, Brillian Sudaryanto . Sudaryanto Sudaryanto Sulis Setiowati Syahroni Hidayat SYAIFULLOH AMIEN PANDEGA PERDANA Tari Mardiana Titik Hinayah Widhi Hartanto Widhia K.Z Oktoeberza Widiyanti, Tanty Yanuar Widyawan Widyawan Wijaya, Adi