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All Journal Seminar Nasional Aplikasi Teknologi Informasi (SNATI) Perfecting a Video Game with Game Metrics Prosiding Seminar Nasional Sains Dan Teknologi Fakultas Teknik Jurnal Pekommas Jurnal Teknologi Informasi dan Ilmu Komputer Journal of ICT Research and Applications Seminar Nasional Informatika (SEMNASIF) Edu Komputika Journal Journal of Information Technology and Computer Science IJID (International Journal on Informatics for Development) Jurnal Akuntansi dan Bisnis IJITEE (International Journal of Information Technology and Electrical Engineering) Journal of Computer System and Informatics (JoSYC) Water Circulation Monitoring System in The Vanname Shrimp Cultivation based on Internet of Thing Jurnal Nasional Teknik Elektro dan Teknologi Informasi Proceeding ISETH (International Summit on Science, Technology, and Humanity) Electrician : Jurnal Rekayasa dan Teknologi Elektro
Indriana Hidayah
Department Of Electrical Engineering And Information Technology, Faculty Of Engineering, Universitas Gadjah Mada, Bulaksumur, Yogyakarta 55281,
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Medicine & Pharmacology Social Sciences Other

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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

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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.
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.
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.
A Literature Review of Knowledge Tracing for Student Modeling : Research Trends, Models, Datasets, and Challenges Ebedia Hilda Am; Indriana Hidayah; Sri Suning Kusumawardani
Journal of Information Technology and Computer Science Vol. 6 No. 2: August 2021
Publisher : Faculty of Computer Science (FILKOM) Brawijaya University

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (900.101 KB) | DOI: 10.25126/jitecs.202162344

Abstract

Modeling students' knowledge is a fundamental part of online learning platforms. Knowledge tracing is an application of student modeling which renowned for its ability to trace students' knowledge. Knowledge tracing ability can be used in online learning platforms for predicting learning performance and providing adaptive learning. Due to the wide uses of knowledge tracing in student modeling, this study aims to understand the state-of-the-art and future research of knowledge tracing. This study focused on reviewing 24 studies published between 2017 to the third quarter of 2021 in four digital databases. The selected studies have been filtered using inclusion and exclusion criteria. Several previous studies have shown that there are two approaches used in knowledge tracing, including probabilistic and deep learning. Bayesian Knowledge Tracing model is the most widely used in the probabilistic approach, while the Deep Knowledge Tracing model is the most popular model in the deep learning approach. Meanwhile, ASSISTments 2009–2010 is the most frequently tested dataset for probabilistic and deep learning approaches. In the future, additional studies are required to explore several models which have been developed previously. Therefore this study provides direction for future research of each existing approach.
Analisis Kesuksesan Implementasi Sistem Informasi Skripsi pada Program Studi Teknik Informatika Universitas Pembangunan Nasional “Veteran” Yogyakarta Rio Jumardi; Eko Nugroho; Indriana Hidayah
Seminar Nasional Aplikasi Teknologi Informasi (SNATI) 2015
Publisher : Jurusan Teknik Informatika, Fakultas Teknologi Industri, Universitas Islam Indonesia

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Abstract

Abstract—Sejak tahun 2007, program studi TeknikInformatika UPN “Veteran” Yogyakarta telahmengimplementasikan Sistem Informasi Skrispi untukmendukung kinerja organisasi yang berkaitan denganakomodasi kebutuhan layanan skripsi. Namun padakenyataannya Sistem Informasi Skripsi belum dimanfaatkansecara maksimal. Evaluasi terhadap Sistem Informasi Skripsiperlu dilakukan untuk mengetahui faktor-faktor apa saja yangmempengaruhi kepuasan pengguna Sistem Informasi Skripsi.Penelitian ini menggunakan modifikasi model penelitiankesuksesan sistem informasi Delon dan Mclean. Modifikasiyang dilakukan adalah dengan menghilangkan variabel usedari model penelitian. Variabel use dihilangkan karena SistemInformasi Skripsi bersifat mandatory. Terdapat lima variabeldalam penelitian ini yaitu, kualitas informasi, kualitas sistem,kualitas layanan, kepuasan pengguna dan net benefit.Penelitian dilakukan dengan menganalisis hasil kuesioner yangterkumpul dari 45 responden yaitu mahasiswa yang pernahmenggunakan Sistem Informasi Skripsi. Metode analisis yangdigunakan adalah Partial Least Square menggunakan softwareSmartPLS.Hasil analisis menunjukkan kepuasan pengguna SistemInformasi Skripsi dipengaruhi oleh kualitas informasi dankualitas sistem. Net benefit dipengaruhi oleh kepuasanpengguna. Dalam penelitian ini kualitas layanan tidakmempunyai pengaruh terhadap kepuasan pengguna. Secaraumum Sistem Informasi Skripsi telah memberikan manfaatkepada pengguna, namun dalam implementasi SistemInformasi Skripsi mahasiswa sebagai pengguna merasa perluadanya peningkatan kualitas layanan dari Sistem InformasiSkripsi.Keyword—kepuasan pengguna; sistem informasi skripsi;Model Delon dan Mclean; PLS
ANALISIS CRITICAL SUCCESS FACTORS IMPLEMENTAS E-PROCUREMENT DI KABUPATEN PROBOLINGGO Sri Kustanti; Hanung Adi Nugroho; Indriana Hidayah
Seminar Nasional Informatika (SEMNASIF) Vol 1, No 1 (2014): Business Intelligence
Publisher : Jurusan Teknik Informatika

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Abstract

E-procurement merupakan proses pengadaan barang/jasa yang pelaksanaannya dilakukan secara elektronik berbasis web/internet. Tujuan penelitian ini adalah untuk menganalisis critical success factors (CSF) implementasi e-procurement di Kabupaten Probolinggo. Penelitian ini bersifat kuantitatif dengan metode survei menggunakan kuesioner yang dibagikan dalam bentuk kuesioner elektronik melalui email responden. Responden adalah pengguna dari sistem pengadaan secara elektronik (SPSE) yaitu Penyedia dan Non penyedia. Analisis data menggunakan Structure Equation model (SEM) dengan tools SmartPLS 2.0. Hasil penelitian yang diperolah menyatakan bahwa implementasi e-procurement di Kabupaten Probolinggo dipengaruhi oleh CSF penerimaan oleh pengguna akhir dan pelatihan, kesesuaian terhadap best practice, integrasi sistem, penyusunan ulang proses pengadaan dan strategi implementasi e-procurement. selain itu ada faktor pendukung lain yang secara tidak langsung mempengaruhi implementasi e-procurement di Kbupaten Probolinggo ialah faktor keamanan/keaslian dokumen pengadaan dan pengukuran kinerja. Faktor keamanan dan keaslian dokumen pengadaan merupakan software default pada Sistem Pengadaan Barang dan jasa (SPSE) yang berkerjasama dengan Lembaga Sandi Negara mengamankan setiap file yang diunggah dan didownload pada proses pengadaan barang dan jasa secara elektronik. sedangkan faktor pengukuran kinerja diasumsikan bahwa implementasi e-procurement tidak lepas dari peran serta Tim LPSE Kabupaten Probolinggo yang mengawal LPSE mulai dari terbentuk hingga sekarang ini dan ini diluar konteks Top Level Manajemen.
Sistem Pendukung Keputusan Uang Kuliah Tunggal dengan Metode Activity Based Costing Winarno Winarno; Eko Nugroho; Indriana Hidayah
Jurnal Akuntansi dan Bisnis Vol 14, No 2 (2014)
Publisher : Accounting Study Program, Faculty Economics and Business, Universitas Sebelas Maret

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.20961/jab.v14i2.156

Abstract

Higher education is one of education institution with tight competition. University have to increase their quality to captivate the public. In addition to consideration of the quality of higher education, the cost of education is also a consideration for choosing a university. In 2012, Indonesia has 33 universities in a form of Badan Layanan Umum (BLU). According to PMK no 76 2008, BLU must have a Cost Accounting System which generates unit cost. It’s used by university to define their tuition fee. In 2013 many of BLU universities don’t have a cost structure analysis. The research goal is designing, creating application that can define unit cost in a BLU university with Activity Based Costing (ABC) method. The research use PHP as programming language and MySQL as database engine. The result of this research is design the system, prototype and testing the application has already done. The System contains define cost structure template, input cost structure and simulate the student tuition fee. The system can help university to define their unit cost. The system has been tested using black box method is done at the level of departments, faculties, bureaus, and divisions in Sebelas Maret University.
ANALISIS PENGARUH SELEKSI FITUR PADA KLASIFIKASI KONSENTRASI SPERMA BERDASARKAN FAKTOR FAKTOR LINGKUNGAN, KESEHATAN, DAN GAYA HIDUP Nasrokhah Noviati; Silmi Fauziati; Indriana Hidayah
Prosiding SNST Fakultas Teknik Vol 1, No 1 (2015): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 6 2015
Publisher : Prosiding SNST Fakultas Teknik

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Abstract

Menurunnya fertilitas terjadi di banyak negara. Berbagai penyebab yang melatarbelakangi hal tersebut. Beberapa di antaranya adalah disebabkan gaya hidup yang buruk, latar belakang kesehatan yang tidak baik, dan juga lingkungan yang tidak sehat. Menggunakan metode data mining, dapat mengklasifikasikan konsentrasi sperma apakah normal atau tidak. Fitur yang banyak dalam dataset akan menimbulkan banyak permasalahan, sehingga perlu melakukan seleksi fitur. Keuntungan menggunakan seleksi fitur antara lain dapat mengingkatkan akurasi suatu klasifikasi, dan membantu mengurangi fitur-fitur yang tidak relevan. Algoritme seleksi fitur yang digunakan dalam penelitian ini Principal Component Alanalisys (PCA) yang diterapkan pada metode klasifikasi Multilayer Perceptron (MLP), Decision Tree, dan Support Vector Machines (SVM). Dataset yang digunakan diambil dari dataset fertility pada UCI Maching Learning Repository untuk mengklasifikasikan konsentrasi sperma. Kesimpulan pada penelitian ini adalah menggunakan seleksi fitur PCA mampu mengurangi fitur yang kurang relevan dari 9 fitur menjadi 5 fitur terbaik yaitu musim, penyakit, kecelakaan, demam, dan rokok. Serta 6 fitur terbaik yaitu musim, penyakit, kecelakaan, demam, rokok, dan operasi. Penggunaan  5 atau 6 fitur terpilih terbukti mampu meningkatkan akurasi dari hasil klasifikasi tanpa seleksi fitur. Kata kunci: data mining,  fertilitas, seleksi fitur, PCA.
KAJIAN PUSTAKA METODE SEGMENTASI CITRA PADA MRI TUMOR OTAK Diah Priyawati; Indah Soesanti; Indriana Hidayah
Prosiding SNST Fakultas Teknik Vol 1, No 1 (2015): PROSIDING SEMINAR NASIONAL SAINS DAN TEKNOLOGI 6 2015
Publisher : Prosiding SNST Fakultas Teknik

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Magnetic Resonance Images (MRI) marupakan mesin terbaik dalam mendiagnosa tumor otak. Namun Interpretasi MRI membutuhkan waktu lama, dan sulitnya mendeteksi adanya edema. Edema adalah jaringan yang berada di dekat sel tumor aktif dan tumpang tindih dengan jaringan normal. Saat ini proses diagnosa citra MRI masih mengandalkan kemampuan pakar radiologi secara manual. Hal ini membutuhkan waktu lama, dan keputusan yang diambil dapat bersifat subjektif.  Sehingga dibutuhkan sistem yang mampu melakukan segmentasi citra MRI untuk membagi daerah-daerah citra menjadi beberapa bagian yang homogen. Pada makalah ini akan dijelaskan metode-metode segmentasi citra pada MRI tumor otak. Penjelasan akan dimulai dari pemahaman tumor otak, peralatan penghasil citra otak seperti MRI, dan metode-metode segmentasi yang pernah dilakukan peneliti sebelumnya. Metode pengklasteran dapat menjadi salah satu pendekatannya. Dan pengklasteran fuzzy merupakan metode yang unggul untuk segmentasi citra tumor otak. Kata kunci: MRI, segmentasi, tumor otak
Co-Authors Adhistya Erna Permanasari Astrie Kusuma Dewi Bobby Rian Dewangga Cahyani Windarto Diah Priyawati Ebedia Hilda Am Eko Nugroho Eko Wahyu Nugroho Emi Iryanti Fachrul Budi Prayoga Fahmi Dhimas Irnawan Faizal M. Priyowibowo Hanung Adi Nugroho Hanung Adi Nugroho Hanung Adi Nugroho Indah Soesanti Intan Sulistyaningrum Sakkinah La Ode Mohamad Zulfiqar Lukito Edi Nugroho M. Arifin Hidayat M. Tsana'uddin Farid Muhammad Haris Diponegoro Nasrokhah Noviati Paulus Insap Santosa Rio Jumardi Ripto Mukti Wibowo Sanny Hikmawati Saucha Diwandari Septian Rico Hernawan Shindy Arti Silmi Fauziati Sri Kustanti Sri Suning Kusumawardani Stefani, Brillian Stefani, Brillian Tari Mardiana Teguh Bharata Adji Widiyanti, Tanty Yanuar Winarno Winarno