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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Jurnal Informatika INFOKAM Jurnal Inspiration SISFOTENIKA CogITo Smart Journal JITK (Jurnal Ilmu Pengetahuan dan Komputer) Jurnal Informatika Universitas Pamulang RESEARCH : Computer, Information System & Technology Management DoubleClick : Journal of Computer and Information Technology JurTI (JURNAL TEKNOLOGI INFORMASI) MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer Jurnal Nasional Komputasi dan Teknologi Informasi JURTEKSI Jurnal Riset Informatika EKUITAS (Jurnal Ekonomi dan Keuangan) Informasi Interaktif CCIT (Creative Communication and Innovative Technology) Journal JMAI (Jurnal Multimedia & Artificial Intelligence) METIK JURNAL Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi SENSITEK Infotekmesin Jurnal Manajemen Informatika dan Sistem Informasi Journal of Information Systems and Informatics KURVATEK Indonesian Journal of Business Intelligence (IJUBI) Jurnal Tecnoscienza Indonesian Journal of Electrical Engineering and Computer Science Jurnal Informa: Jurnal Penelitian dan Pengabdian Masyarakat Infotek : Jurnal Informatika dan Teknologi Jurnal TIKOMSIN (Teknologi Informasi dan Komunikasi Sinar Nusantara) Jurnal Teknologi Informatika dan Komputer Jurnal Teknimedia: Teknologi Informasi dan Multimedia Journal of Applied Data Sciences International Journal of Computer and Information System (IJCIS) Bit (Fakultas Teknologi Informasi Universitas Budi Luhur) Jurnal Ilmiah IT CIDA : Diseminasi Teknologi Informasi Jurnal Informatika Teknologi dan Sains (Jinteks) EXPLORE Innovative: Journal Of Social Science Research Nusantara Journal of Computers and its Applications Jurnal INFOTEL Journal of Innovative and Creativity Explore
Kusrini Kusrini
Universitas AMIKOM Yogyakarta
Aerospace Engineering Humanities Automotive Engineering 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 Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Mathematics Mechanical Engineering Neuroscience Public Health Social Sciences Transportation Other

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PENERAPAN ALGORITMA K NEAREST NEIGHBOR UNTUK REKOMENDASI MINAT KONSENTRASI DI PROGRAM STUDI TEKNIK INFORMATIKA UNIVERSITAS PGRI YOGYAKARTA Adi Prasetyo; Kusrini Kusrini; M Rudyanto Arief
Informasi Interaktif Vol 4, No 1 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

The graduation time of a student is very important because it relates to many parties, in addition to the students concerned, guardian lecturers, and program study programs and related parties in the time of student graduation. one of the factors which is the result of graduation from informatics engineering study program students is that many students are wrong in taking thesis titles that are not in accordance with their interests and concentration. So research needs to be done to solve this problem. One way to solve problems is to make predictions. Selection of interest by using student data using Case Base Reasoning (CBR). In this study applying the K-NN Algorithm in determining recommendations in choosing the concentration. The results obtained from this study are by classification using the K-NN Algorithm obtained concentrations that match their interests.  Keywords : Predictions, Case Base Reasoning, K-NN Algorithm
SISTEM PENDUKUNG KEPUTUSAN UNTUK MENENTUKAN PEMILIHAN JURUSAN PADA UNIVERSITAS DENGAN MENGGUNAKAN METODE NAÏVE BAYES Devina Ninosari; Kusrini Kusrini; M. Rudiyanto Arief
Informasi Interaktif Vol 3, No 3 (2018): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

In general, prospective new students are confused in choosing a department that suits the needs and abilities of the academic. Typically, the selection of majors is often chosen because the favorite departmental factors are mixed with prospective students' goals, costs and also not tested in the opinion so that later consequences will be found in the new student candidates because of wrong in choosing the majors are Stop college, Droup Out and leave and move majors. To facilitate the process of choosing the right majors and in accordance with the ability of prospective new students in choosing the department then the researcher will make the Decision Support System Support System Selection by using the Naïve Bayes Algorithm to find out new student candidates to be obtained the probability is accepted and not accepted. classify and acceptable and unacceptable decisions.From the results of calculations with naïve bayes algorithm for data samples 2016/2017 obtained 3 students. 1 in D3MI and 2 major in Inforatika Engineering and 7 people not accepted. After obtained the result of calculation by using 10 sample data hence the next researcher doing process accuracy level obtained in this research there is accuracy value equal to 70%.  Keywords: sistem Pendukung Keputusan, Pemilihan Jurusan, calon mhasiswa baru, Naïve Bayes.
PREDIKSI CUSTOMER CHURN PERUSAHAAN TELEKOMUNIKASI MENGGUNAKAN NAÏVE BAYES DAN K-NEAREST NEIGHBOR Kaharudin Kaharudin; Musthofa Galih Pradana; Kusrini Kusrini
Informasi Interaktif Vol 4, No 3 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

For a company it is very vital. Customers are the key to running a business that is run. But in reality there are loyal customers and some are churned out. Churn is defined as the tendency of customers to stop doing business with a company. It is important for companies to be able to identify customers who have a tendency to be churn customers. Then a report is needed to be able to identify and make decisions for management. Prediction method using Naïve Bayes method produces an accuracy of 76% and K-Nearest Neighbor produces information with a K = 1 value of 73%, K = 3 which is 76% and K = 5 by 78% It can be concluded that the K-Nearest Neighbor Method with K = 3 has a better value. The results of customer predictions for a company can be used to take an example for the customer so they will not churn.  Keywords: Prediction, Customer, Churn, Naïve Bayes, Telecomunication, K-Nearest Neighbor.
PERENCANAAN DAN PENGEMBANGAN ARSITEKTUR PELAYANAN INFORMASI ALUMNI PADA UNIVERSITAS YAPIS PAPUA - JAYAPURA Riandi Widiantoro; Kusrini Kusrini; Sudarmawan Sudarmawan
Informasi Interaktif Vol 3, No 2 (2018): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

The importance of the role of technology and information systems within the organization to support business activities so as to provide services in accordance with organizational goals, IKA Uniyap is an alumni development organization of Yapis University of Papua, in carrying out its role of IKA Uniyap has not fully utilized the information system.The togaf framework is a method that is able to design and manage technology architecture and information system so that it can describe a good architectural model and can be used by the organization to achieve the goal.The discussion in this research is making documentation of technology architecture planning and information system of alumni service of Yapis University of Papua using togaf framework, with result of discussion that is scope of business process covering 2 main activity and supporting, defining architectural principles and vision, mapping business strategy solution, defining current business conditions and proposed target business architecture, information systems architecture and technology architecture. Keyword: Arsitektur bisnis, Arsitektur sistem informasi, arsitektur teknologi, TOGAF ADM
KLASIFIKASI JENIS REMPAH-REMPAH BERDASARKAN FITUR WARNA RGB DAN TEKSTUR MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR Kaharruddin Kaharruddin; Kusrini Kusrini; Emha Taufiq Luthfi
Informasi Interaktif Vol 4, No 1 (2019): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

 Indonesia is a country famous for its spices wealth, spices have many benefits such as cooking and can also be used as medicine, but nowadays there are many Indonesian people who cannot distinguish each type of spices especially the rhizomes that will be used due to their shape quite similar, even though the selection of the right type of spices in accordance with the needs is very important because the spices used for cooking or medicine have different taste and efficacy, therefore the use of computer technology needs to be used to facilitate and accelerate humans in conducting classifications, this research classifies spices based on RGB and Texture colors using K-Nearest Neighbor Algorithm and distance measurement using Euclidean Distance, from 30 times the test experiment gets the result that the level of truth with K = 1 is 76%, K = 3 is equal to 67% and K = 5 by 63%. From these results it is known that based on GE colors and computer textures can classify spices but with a fairly low accuracy so that further development is needed such as adding form features. Keywords: classification, spices, K-Nearest Neighbor.
SENTIMEN ANALISIS REVIEW PENGGUNA MARKETPLACE ONLINE MENGGUNAKAN NAÏVE BAYES CLASSIFIER Siti Rahayu; Kusrini Kusrini; Heri Sismoro
Informasi Interaktif Vol 3, No 3 (2018): Jurnal Informasi Interaktif
Publisher : Universitas Janabadra

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Abstract

The presence of social media allows users to freely review, comment on works, public figures, products, and companies. This is why then social media now is become a great source of data, which can be collected for some analysis , according to information needed. One of them is , Social media data can be used to evaluate the fast-growing market-place website in Indonesia. This can be done by analyzing the sentiments of the users review in social media sites especially twitter. The object of this research is marketplace Online Shoope. The activity of analyzing and processing user review data can also be referred to as Sentiment analysis / opinion mining. One method of text mining that can be used to solve the problem mining issue is Naïve Bayes Classifier (NBC). NBC can be used to classify opinions into positive and negative opinions. The level of accuracy of sentiment analysis system of user review of Marketplace Online shopee by using Naïve Bayes Classifier is 78,3% , with 47 tweets are classified accurately from the total number of testing tweet as much as 60 tweets, with the amount of training data are 300 tweets.Keywords: Naïve Bayes Classifier, Sentiment Analysis, Marketplace Online
Pemodelan Sistem Informasi Manajemen Sparepart ATM Menggunakan Zachman Framework pada Logistik ASP Ambo Ridlan Ahmad; Kusrini Kusrini
METIK JURNAL Vol 3 No 1 (2019): METIK Jurnal
Publisher : LP3M Universitas Mulia

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Abstract

Logistik ASP Ambon merupakan tempat penitipan sparepart ATM yang berada di kota Ambon. Manajemen sparepart ATM pada Logistik ASP Ambon telah memiliki prosedur data masuk dan data keluar sparepart ATM yang baik, akan tetapi sistem tersebut masih dilakukan secara konvensional. yang menyebabkan selisih, kekosongan part dan kehilangan data sparepart yang sangat besar. Tujuan dilakukannya penelitian ini yaitu untuk menganalisis kebutuhan dalam memanajemen seluruh data part serta menghasilkan perancangan Enterprise Architecture (EA) Model Sistem Informasi Manajemen (SIM) Sparepart ATM pada Logistik ASP Ambon. Analisis yang dilakukan bertujuan untuk memperoleh informasi-informasi terhadap permasalahan penelitian dan mendefinisikan kebutuhan dengan tujuan untuk menganalisis kebutuhan sistem dan mengetahui kelemahan dari sistem lama atau yang sedang berjalan sedangkan untuk mengurai kebutuhan EA dalam merancang arsitektur Model SIM Sparepart ATM menggunakan pendekatan arsitektur Zachman Framework. Selanjutnya, untuk menguji kelayakan terhadap arsitektur tersebut digunakan metode EA Score Card. Hasil dari analisis dan rancangan yang dilakukan, dihasilkan suatu Model SIM Sparepart ATM yang dapat mengontrol seluruh aktivitasaktivitas part secara optimal yang terdiri dari data stok barang, data penggunaan part, data part masuk dan data return part. Berdasarkan hasil pengujian kelayakan yang dilakukan terhadap perancangan arsitektur tersebut dengan menggunakan EA Score Card menunjukkan bahwa perancangan arsitektur tersebut layak untuk diimplementasikan menjadi sebuah SIM Sparepart ATMdengan rata-rata hasil pengujian sebesar76,18%. Dengan menggunakan pendekatan EA Zachman Framework, diperoleh informasi secara detail dalam mengurai kebutuhan perancangan SIM Sparepart ATM, serta segala kebutuhan baik data, sumber daya manusia dan infrastruktur yang mendukung berjalannya sistem informasi tersebut.
Prediksi Indeks Harga Konsumen Komoditas Makanan di Kota Surabaya menggunakan Support Vector Regression Ayu Adelina Suyono; Kusrini Kusrini; Muhammad Rudyanto Arief
METIK JURNAL Vol 6 No 1 (2022): METIK Jurnal
Publisher : LPPM Universitas Mulia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47002/metik.v6i1.339

Abstract

In data mining, predictions are known to find knowledge about what will happen in the future. Predictions are usually made on time-series data. The Consumer Price Index (CPI) is an index value derived from daily consumer price data. The results of the CPI calculation are derived from observations of commodity prices at the household consumer level, which are carried out routinely on a daily, weekly, bi-weekly, and monthly basis. CPI prediction can be done using a data mining algorithm, namely Support Vector Regression (SVR). SVR is part of the Support Vector Machine algorithm that functions to solve regression cases. SVR is a reliable algorithm in the case of regression because it can handle data overfitting well. The data used as input in this paper comes from 34 food commodity prices, and the output data is obtained from the CPI value data. The food commodity price data used is from Surabaya City. The data period used is from 2014-2020. The results of the implementation of SVR with 4 kernels show that the Polynomial kernel has the best error rate with a MAPE value of 4.31%.
Analisis Spektrum Perintah Suara Berdasarkan Gender Menggunakan Algoritma K-Nearest Neighbours Candra Adipradana; Teguh Sri Pamungkas; Achmad Wazirul Hidayat; Pawit Srentiyono; Kusrini Kusrini
JURNAL TECNOSCIENZA Vol. 4 No. 1 (2019): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

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Abstract

Perintah suara merupakan salah satu media pengoperasian sistem automation yang banyak diminati. Pengoperasian sistem yang mudah dan tidak membutuhkan banyak tenaga merupakan alasan utama perintah suara cocok digunakan untuk sistem automation. voice recognition dapat digambarkan sebagai suatu proses di mana mesin atau program menerima dan menafsirkan dikte serta memahami dan menjalankan perintah yang diucapkan. Penggunaaan suara adalah suatu cara berkomunikasi yang paling sering dilakukan oleh manusia. Dimana penelitian dibidang pengelolahan suara telah memotivasi banyak orang untuk menciptakan model mekanik untuk meniru kemampuan komunikasi verbal manusia. Tujuan penelitian ini yaitu untuk memperoleh informasi dari hasil spectrum perintah suara manusia, sehingga mampu di terapkan pada sistem automation seperti home automation, smart wheelchair dan peralatan automation lainnya yang mampu memahami bahasa manusia sehingga dapat menjalankan apa yang pengguna perintahkan. Dari hasil penelitian ini menghasilkan untuk kelompok pria dan wanita durasi waktu yang dibutuhkan rata-rata menunjukkan nilai berkisar antara 0,5344 – 0,8217 (pria) dan 0,9001 – 1,1135 seconds (perempuan), artinya bahwa pria lebih membutuhkan sedikit waktu dalam mengeluarkan perintah suara dibandingkan wanita. Nilai Kedekatan dengan standar perintah suara untuk Pria dan wanita hampir memiliki kesamaan yaitu 1,00 hingga 3,00 atau 97 % - 99 %. Artinya tidak terlalu adanya perbedaan yang cukup signifikan ketika pria dan wanita menghasilkan suara yang disesuaikan dengan pedoman perintah suara. Sehingga jika metode ini diterapkan untuk sistem Voice Recognition, metode ini dapat menangkap suara yang diberikan oleh pengguna dengan cukup akurat yaitu diatas 90% akurasi Kata kunci: deteksi suara, pengenalan suara, perintah suara, sistem deteksi suara, audacity
Tinjuan Pustaka Sistematis - Sistem Rekomendasi Menggunakan Collaborative Filtering Fiyas Mahananing Puri; Kusrini Kusrini; Emha Taufiq Luthfi
JURNAL TECNOSCIENZA Vol. 5 No. 1 (2020): TECNOSCIENZA
Publisher : JURNAL TECNOSCIENZA

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Abstract

Pemanfaatan bigdata pada era industry 4.0 telah banyak diterapkan diberbagai bidang untuk membuat sebuah sistem rekomendasi, salah satunya pada bidang bisnis. Collaborative Filtering merupakan salah satu metode yang banyak digunakan pada saat ini untuk menghasilkan sebuah rekomendasi produk. Graph Database pada saat ini sudah menjadi pilihan yang banyak dikombinasikan dengan penggunaan metode Collaborative Filtering. Tujuan dari makalah ini adalah sebagai systematic literatur review untuk menentukan sebuah sistem rekomendasi dengan menggabungkan metode rekomendasi dengan database grafik. Hasil penelitian ini menjawab pertanyaan penelitian (Research Question) sebagai berikut. RQ1: Apakah penggunakan sistem rekomendasi dengan algoritma collaborative filtering mengalami peningkatan? RQ2: Apasajakah fokus dan tujuan penelitian dengan menggunakan collaborative filtering? RQ3: Sub-disiplin ilmu apa yang sering menggunakan collaborative filtering? Sebagai hasil dari tinjauan pustaka, 42 jurnal dipilih sebagai bahan Analisa yang diterbitkan antara tahun 2014 sampai dengan 2019. Hasil penelitian menunjukkan adanya keakuratan tingkat rekomendasi dari metode dan algoritma yang digunakan, dan menjawab pertanyaan selanjutnya (RQ2 dan RQ3). Kata kunci: Sistem Rekomendasi, Collaborative Filtering, Graph Database
Co-Authors Abdi Firdaus Achmad Wazirul Hidayat Adadilaga Arya Priwanegara Adhien Kenya Estetikha Aditya Hastami Ruger Aflahah Apriliyani Agatha Deolika Agianto Syam Halim Agung Budi Prasetyo Agus Susilo Nugroho Ajie Kusuma Wardhana Akrilvalerat Deainert Wierfi Alfahmi Muhammad Arif Alva Hendi Muhammad Amir Bagja Andi Bahtiar Semma Andi Sunyoto Andi Suyoto Andri Rachmatullah Andris Faesal Anggit Dwi Hartanto Anjar Anjani Putra Anwar Sadad Aolia Ikhwanudin Arham Rahim Arief Setyanto Arif Fajar Solikin Arnila Sandi Asro Nasiri Asro Nasrini Ayu Adelina Suyono Aziz Muslim Bimantyoso Hamdikatama Candra Adipradana Dedi Gunawan Devina Ninosari Dimaz Arno Prasetio Dina Maulina Donny Yulianto Dwi Astuti Dwi Utami Dwinda Etika Profesi Eka Wahyu Sholeha Eko Pramono Elik Hari Muktafin Ema Utami Emha Taufiq Luthfi Emha Taufiq Luthfii Erwin Apriliyanto Fandli Supandi Fendy Prasetyo Nugroho Ferry Wahyu Wibowo Fiyas Mahananing Puri Guido Adolfus Suni Hadryan Eddy Hafidz Sanjaya, Hafidz Hanafi Hanafi Hanif Al Fatta Hasirun Hasirun Henderi . Hendrik Hendrik Heri Sismoro Hery Nurmawan Hery Siswanto I Made Artha Agastya I Putu Agus Ari Mahendra Ichsan Wasiso Idris Idris Imam Listiono Irma Darmayanti Irwan Oyong José Ramón Martínez Salio Juwari Juwari Kaharuddin Kanafi Kanafi Khoirun Nisa Khomsatun Khomsatun Kumara Ari Yuana Kusnawi Kusnawi Kusuma Chandra Kirana M rudyanto Arief M. Idris Purwanto M. Nurul Wathani M. Rudiyanto Arief M. Rudyanto Arief M. Zainal Arifin Mahmudi Mahmudi Mansur Mansur Marwan Noor Fauzy Maykel Sonobe Mei P Kurniawan Mei P. Kurniawan MEI PARWANTO KURNIAWAN Moh. Badri Tamam Muahidin, Zumratul Muh Saerozi Muhamad Fatahillah Z Muhamad Yusuf Muhammad Fajrian Noor Muhammad Mariko Muhammad Riandi Widiyantoro Muhammad Riza Eko S Muhammad Rudyanto Arief Mukti Ali Mulia Sulistiyono Muqorobin Muqorobin Muslihah, Isnawati Musthofa Galih Pradana Nanang Prasetiyantara Neno, Friden Elefri Nibras Faiq Muhammad Noor Abdul Haris Nova Agustina Noviyanti P. Nur Hamid Sutanto Paradise, Paradise Patmawati Hasan Pawit Srentiyono Prabowo Budi Utomo Pramono Pramono Prasetio, Agung Budi Prasetyo, Adi Prastowo, Wahit Desta Reflan Nuari Retzi Yosia Lewu Ridlan Ahmad Rifan Ferryawan Ripto Sudiyarno Rita Wati Riyan Abdul Aziz Rizki Mawan Robi Wariyanto Abdullah Rona Guines Purnasiwi Rudyanto Arief Saikin Sigit Pambudi Simone Martin Marotta Siti Fatonah Siti Hartinah Siti Rahayu Siti Rokhmah Slamet Slamet Sri Handayani Sri Wulandari Sry Faslia Hamka Sudarmawan Sudarmawan Sudarmawan Sudarmawan Sudiana Sudiana Sugi Harsono Supriantara Supriantara Supriatin Supriatin Supriyati Supriyati Syaiful Ramadhan Teguh Sri Pamungkas Tito Prabowo TONNY HIDAYAT Tri Andi Tri Anggoro Tri Haryanti Tutik Maryana Tutut Dwi Prihatin Umdatur Rosyidah Vera Wati Victor Saputra Ginting Wahyu Adie Saputro Walidy Rahman Hakim Widdi Djatmiko Winarnie Yovita Kinanti Kumarahadi Yudha Chirstianto F Yuliana Yulita Fatma Andriani Yulius Nahak tetik Yuni Ambar S Yusrinnatul Jinana triadin Yusuf Fadlila Rachman Zul Hisyam Zulkipli Zulkipli