Article Per Year (5 Year)
p-Index From 2020 - 2025
3.036
P-Index
This Author published in this journals
All Journal Jurnal Informatika Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Techno.Com: Jurnal Teknologi Informasi Jurnal Transformatika Jurnal Edukasi dan Penelitian Informatika (JEPIN) Journal of Information Systems Engineering and Business Intelligence Riau Journal of Computer Science Jurnal Teknologi dan Sistem Komputer JUTEI (Jurnal Terapan Teknologi Informasi) Jurnal ULTIMA InfoSys Jurnal ULTIMATICS Jurnal Informatika Jurnal Informatika dan Sistem Informasi COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Prosiding Seminar Nasional Program Pengabdian Masyarakat Abdimas Altruis: Jurnal Pengabdian Kepada Masyarakat Jurnal Atma Inovasia GIAT: Teknologi untuk Masyarakat International Journal of Information Technology and Computer Science Applications (IJITCSA) SmartComp International Journal of Information Technology and Business JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Yuan Lukito
Universitas Kristen Duta Wacana
Agriculture, Biological Sciences & Forestry Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Medicine & Pharmacology Social Sciences Other

Published : 33 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 33 Documents
Search

 1   2   3   4 
ANALYSIS OF MTF, MTF-1 AND MTF-2 ALGORITHM ON BURROWS WHEELER COMPRESSION Sagara Mahardika Sunaryo; Lukas Chrisantyo; Yuan Lukito
Jurnal Terapan Teknologi Informasi Vol 3 No 1 (2019): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (885.84 KB) | DOI: 10.21460/jutei.2019.31.148

Abstract

Kebutuhan kompresi data teks di era komputasi awan saat ini masih cukup tinggi. Data teks perlu dikompresi sekecil mungkin agar mudah dikirimkan. Burrows Wheeler Compression Algorithm (BWCA) adalah salah satu algoritma kompresi teks jenis block sorting yang bersifat non-proprietary dan cukup populer digunakan. Dalam prosesnya, BWCA menggunakan metode pemrosesan awal yang disebut Global Structure Transformation (GST) untuk menyusun karakter agar lebih baik hasil kompresinya. Penelitian ini membandingkan tiga metode pemrosesan awal Move-to-Front, yaitu MTF, MTF-1 dan MTF-2. Bahan uji kompresi berupa data Alkitab Bahasa Inggris, Indonesia dan Jawa, dan beberapa data yang berasal dari Calgary Corpus. Oleh karena kompresi teks adalah kompresi yang bersifat lossless dan reversibel, maka selain melakukan pengujian untuk pengompresian data, juga dilakukan pengujian untuk pendekompresian data dengan Inverse Burrows Wheeler Transform. Pengujian kompresi dan dekompresi pada data Alkitab maupun Calgary Corpus berhasil dilakukan dan menunjukkan MTF-1 mampu memberikan rasio kompresi yang lebih baik dikarenakan jumlah total tiap bit pada proses Huffman lebih sedikit dibandingkan dua metode lainnya.
Building Data Warehouse and Dashboard of Church Congregation Data Ragil Yoga Irawan; Budi Susanto; Yuan Lukito
Jurnal Terapan Teknologi Informasi Vol 3 No 2 (2019): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21460/jutei.2019.32.183

Abstract

A data warehouse is essential for an organization to process and analyze data coming from the organization. Hence, a data warehouse together with a dashboard to visualize the processed data are built to accommodate the need of the church administrator to analyze a large set of church congregation data. The data warehouse is built using the Kimball principle. This Kimball principle emphasizes the implementation of a dimensional model in the data warehouse, not a relational model used in a regular transactional database. An ETL process that contains extract, transform and load processes is used to retrieve all data from the regular transactional database and transform the data so the data can be loaded into the data warehouse. A dashboard is then built to visualize the data from the data warehouse so the users can view the processed data easily. Users can also export the processed data into an excel file that can be downloaded from the dashboard. A web service is built to get data from the data warehouse and return it to the dashboard.
Sistem Prediksi Harga Saham LQ45 Dengan Random Forest Classifier Kevin Valiant; Yuan Lukito; R. Gunawan Santosa
Jurnal Terapan Teknologi Informasi Vol 3 No 2 (2019): Jurnal Terapan Teknologi Informasi
Publisher : Fakultas Teknologi Informasi

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

Data yang digunakan adalah ringkasan saham harian perusahaan yang terdaftar pada indeks LQ45 versi Agustus 2018 – Januari 2019 mulai tanggal 1 Juli 2015 hingga 31 Desember 2018. Technical indicator yang digunakan dalam penelitian ini adalah On-Balance Volume, Chaikin Oscillator, Moving Average Convergence/Divergence, dan Bollinger Bands. Data tersebut kemudian dibentuk modelnya untuk setiap kode saham, rentang waktu, dan tipe fitur. Rentang waktu prediksi yang digunakan adalah 1 hari, 5 hari, dan 20 hari. Tipe fitur yang digunakan untuk membentuk model adalah plain yang menggunakan seluruh nilai ringkasan saham harian dan technical indicator-nya, grouped yang fiturnya merupakan kondisi technical indicator terhadap hari sebelumnya (naik, tetap, dan turun) dan nilainya terhadap garis nol (positif, nol, negatif), serta onehot yang fiturnya merupakan hasil one-hot encoding terhadap fitur grouped.  Model yang dibentuk kemudian digunakan untuk memprediksi perubahan harga saham dengan kemungkinan nilai naik, tetap, atau turun. Nilai akurasi dihitung menggunakan confusion matrix. Hasil pengujian terhadap data latih menunjukkan nilai yang sangat baik, dimana tipe fitur plain dengan rentang waktu 5 dan 20 hari mencapai 100%. Hasil pengujian terhadap data uji menunjukkan penurunan dibanding data latih, namun tipe fitur plain tetap menunjukkan performa paling baik dimana terdapat tiga kode saham dengan akurasi lebih besar dari 60% untuk rentang waktu satu hari, enam kode saham untuk rentang waktu lima hari, dan empat belas kode saham untuk rentang waktu dua puluh hari, sembilan di antaranya di atas 70%, dengan kode ANTM mencapai akurasi sebesar 80,6%.
ANALISIS PERBANDINGAN MODEL PROGRAM PENDAMPINGAN UNTUK PELATIHAN OSN KOMPUTER SISWA SMA Antonius Rachmat Chrismanto; Katon Wijana; Rosa Delima; Yuan Lukito; Halim Budi Santoso
Prosiding Seminar Nasional Program Pengabdian Masyarakat 2018: 2. Penguatan Inovasi Teknologi (Pangan, Pertanian, Energi, Transportasi) Bagi Pemerintah Daera
Publisher : Universitas Muhammadiyah Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (451.895 KB)

Abstract

ABSTRAK Pendampingan pelatihan Olimpiade Sains Nasional (OSN) Komputer (OSNK) sangat diperlukan bagi para siswa/i dalam berbagai tingkat sekolah, baik SD, SMP, mapupun SMA. Terbatasnya waktu pelatihan dan tenaga pelatih OSNK menjadi hal yang perlu dibantu dan diselesaikan. Pendampingan (mentoring) pelatihan OSNK telah dilakukan oleh tim FTI UKDW pada tahun 2017 dan 2018. Terdapat dua model program pendampingan yaitu model program semesteran yang dilaksanakan pada tahun 2017 dan model program intensif 3 (tiga) hari yang dilaksanakan pada tahun 2018. Pada tulisan ini dilakukan analisis terhadap kedua model program. Aspek yang dianalisis meliputi: 1). Metode dan media yang digunakan, 2). Tingkat partisipasi peserta, 3). Efisiensi dan efektivitas pengelolaan sumber daya, 4). Kelebihan dan kekuranganmasing- masing metode; dan 5). Keberhasilan program kerja. Beberapa kesimpulan yang didapat berdasarkan hasil analisis adalah: 1) Penggunaan aplikasi e-learning Moodle dapat mempermudah dalam penyebaran materi ajar dan mendukung proses evaluasi belajar yang lebih efisien dan akurat. Penggunaan media pembelajaran sebaiknya merupakan kombinasi antara media konvesional dan berbasis online; 2) Tingkat pertisipasi siswa lebih tinggi pada model program intensif; 3) Model program intensif lebih efisien dari segi pembiayaan transport dan komsumsi baik fasilitator maupun siswa; 4) Model program semesteran memberikan waktu yang lebih panjang bagi peserta untuk memahami materi; 5) Program pendampingan OSNK ini berhasil mendukung 7 (tujuh) siswa untuk lolos seleksi OSNK tingkat Kabupaten/Kota.
PENDAMPINGAN DAN PELATIHAN PENGUATAN COMPUTATIONAL THINKING SEBAGAI KEMAMPUAN UTAMA ABAD 21 Antonius Rachmat Chrismanto; Katon Wijana; Eko Verianto; Argo Wibowo; Yuan Lukito
ABDIMAS ALTRUIS: Jurnal Pengabdian Kepada Masyarakat Vol 3, No 2 (2020): Oktober 2020
Publisher : Universitas Sanata Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (282.799 KB) | DOI: 10.24071/aa.v3i2.3210

Abstract

The skills needed in the 21st century are communication, collaborative, creativity innovation, and, critical thinking problem solving. One that is closely related to mindset is critical thinking problem solving. Critical thinking and being able to solve problems means the ability to understand a complex problem, connect information to other information, and finally find the solutions of the problem. This ability is closely related to the field of Information Technology (IT) because this field really needs a structured, coherent mindset, analysis, and computational thinking. This ability is very much needed by the young generation of Indonesia today.The Information Technology of UKDW Faculty (FTI)’s partner, Bopkri I Yogyakarta High School, has the same vision to prepare students to have real and applicable abilities. Students need regular and structured training to achieve these goals. Bopkri I and FTI work together in the form of community service training in strengthening computational thinking by implementing basic programming, advanced programming, and training evaluation.This program was held in 2 major stages., The first stage consists of strengthening computational thinking using basic programming training in general, and the second using advanced programming training in the form of competitive programming and its simulations. Students were given complete material, face-to-face/online knowledge transfer, complete modules, exercises, and direct simulations by some experienced lecturers from FTI UKDW.
Implementasi Sistem Crowdsourced Labelling Berbasis Web dengan Metode Weighted Majority Voting Antonius Rachmat C.; Yuan Lukito
ULTIMA InfoSys Vol 6 No 2 (2015): UltimaInfoSys :Jurnal Ilmu Sistem Informasi
Publisher : Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (949.711 KB) | DOI: 10.31937/si.v6i2.223

Abstract

Crowdsourced Labelling is a large scale data labelling process, solicits a large group of people to label the data, usually via Internet. This paper discusses about design and implementation of Web-based Crowdsourced Labelling. Supervised learning classification methods need labelled training data for its training phase. Unfortunately, in many cases, there aren’t any already available labelled training data. Large scale data labelling is a tedious and time consuming work. This research develops a web-based crowdsourced labelling which able to solicit a large group of people as data labeler to speed up the data labelling process. This system also allows multiple labeler for every data. The final label is calculated using Weighted Majority Voting method. We grabbed and used Facebook comments from the two candidates’ Facebook Page of 2014 Indonesian Presidential Election as testing data. Based on the testing conducted we can conclude that this system is able to handle all the labelling steps well and able to handle collision occurred when multiple labeler labelling a same data in the same time. The system successfully produces final label in CSV format, which can be processed further with many sentiment analysis tools or machine learning tools. Index Terms - Crowdsources labeling, web-based system, supervised learning, weighted majority voting.
Deteksi Komentar Spam Bahasa Indonesia Pada Instagram Menggunakan Naive Bayes Antonius Rachmat C; Yuan Lukito
Ultimatics : Jurnal Teknik Informatika Vol 9 No 1 (2017): Ultimatics: Jurnal Ilmu Teknik Informatika
Publisher : Faculty of Engineering and Informatics, Universitas Multimedia Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (899.718 KB) | DOI: 10.31937/ti.v9i1.564

Abstract

Instagram is the most famous pictures and videos media sharing based on the web & mobile application. Instagram users can have picture posts that can be commented by their followers. Indonesian public figures such as actors, actresses, musicians use Instagram to promote their activities to their followers. Unfortunately, there are a lot of spam comments in Instagram that need special attention and have to be removed. This research grabs Instagram comments and builds the dataset from Indonesian public figures who have more than one million followers. By using preprocessing (tokenization, stop words removal, and stemming), TF-IDF weighting, and supervised learning, Naive Bayes method is used to detect spam comments in Indonesian. Naive Bayes produces 74,31% accuracy rate on unbalanced datasets and 77,25% accuracy rate on balanced datasets. This result shows that Naïve Bayes can be used to build an automatic Indonesian spam comments detector on Instagram with high accuracy rate. The novelty of this research is that Naive Bayes can be used to detect spam comment on our Indonesian Instagram comments dataset. Index Terms—Instagram, Naive Bayes, Indonesian spam comments, spam comments detection.
VERIFIKASI AKUN DATABASE DENGAN PENERAPAN METODE TEMPLATE MATCHING PADA KARATERISTIK WAJAH PERSONAL Ginting Pebrindanov; Widi Hapsari; Yuan Lukito
Jurnal Informatika Vol 11, No 1 (2015): Jurnal Teknologi Komputer dan Informatika
Publisher : Universitas Kristen Duta Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (4638.858 KB) | DOI: 10.21460/inf.2015.111.427

Abstract

The identification of account is a step to keep the important data secure. Nowadays, it can be done by using username and password, but, after see the reality, the using of username and password can’t keep the data secure from the thief. Because of that, the  verification of the characteristic of personal face can be a solution to change the using username and password. The method that can be used for verification is template matching.It is implemented in four features of personal face, such as left eye, right eye, nose and lips. The four images of  each feature will be extracted with wavelet haar method. The feature extraction will be done during template taking process and verification. The result of this research, the result of verification is determined by two factors,  such as the distance between face and web camera is different when the template taking process and the verification process and the diferrent brightness condition when the template taking process and the verification proccess. The threshold value that has been decided is not really able to block the unregistered data.  Then the accuracy of the verification activity is still low and it is still not able yet to identify an account well.
Klasifikasi Sentimen Komentar Politik dari Facebook Page Menggunakan Naive Bayes Antonius Rachmat C; Yuan Lukito
Jurnal Informatika dan Sistem Informasi Vol. 2 No. 2 (2016): Jurnal Informatika dan Sistem Informasi
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (887.635 KB)

Abstract

Seiring maraknya situs media sosial yang digunakan sebagai sarana kampanye politik online maka makin banyak pula daukungan kampanye dari dunia maya melalui berbagai cara.  Cara kampanye yang digunakan para politisi diantaranya adalah melalui Twitter hashtag, petisi di Facebook, atau pembuatan Facebook Page di mana komentarnya dapat di-like/disline oleh para pendukungnya.  Permasalahan yang dibahas pada tulisan ini adalah belum banyaknya sistem yang dapat mengklasifikasikan pro kontra dari komentar-komentar yang terdapat pada Facebook Page.  Pada tulisan ini akan dibahas penggunaan metode Naive Bayes untuk melakukan klasifikasi sentimen positif atau negatif terhadap komentar dari status kampanye politik dari Facebook Page.  Studi kasus yang digunakan pada penelitian ini adalah status dan komentar terhadap Facebok Page calon presiden Republik Indonesia pada Pemilu tahun 2014.  Tahapan penelitian dilakukan dengan pengumpulan data 68 status (3400 komentar) selama masa kampanye, dengan kegiatan preprosesing tokenisasi, stemming, pembobotan token, kemudian dilanjutkan klasifikasi, dan pengujian menggunakan confusion matrix.  Dari hasil implementasi dan pengujian, metode Naive Bayes memiliki tingkat akurasi klasifikasi sentimen mencapai lebih dari 83%.
LQ45 Stock Price Forecasting: A Comparison Study of Arima(p,d,q) and Holt-Winter Method Santosa, Raden Gunawan; Chrismanto, Antonius Rachmat; Raharjo, Willy Sudiarto; Lukito, Yuan
International Journal of Information Technology and Computer Science Applications Vol. 2 No. 2 (2024): May - August 2024
Publisher : Jejaring Penelitian dan Pengabdian Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58776/ijitcsa.v2i2.150

Abstract

The Holt-Winter method and ARIMA(p,d,q) are two frequently used forecasting techniques. When using ARIMA, errors are expected to be connected with earlier errors because it is based on data correlation with prior data (autoregressive) (moving average). The Holt-Winter model comes in two different forms: Multiplicative Holt-Winter and Additive Holt-Winter. No one has ever attempted to compare combined time series and cross-section data, despite the fact that there has been a great deal of prior study on ARIMA and Holt-Winter. In a combined time-series and cross-section dataset, the accuracy rates of Holt-Winter and ARIMA(p,d,q) will be compared in this study. LQ45 stock prices are used because they track the performance of 45 stocks with substantial liquidity, sizable market caps, and solid underlying businesses. The Mean Absolute Percentage Error (MAPE) method is used to gauge accuracy. This study contributes to MAPE exploration by using a Boxplot diagram from cross-sectional data. With the Boxplot diagram, we can see the MAPE spread, the MAPE's center point, and the presence of outliers from the MAPE of LQ45 stock. According to the findings of this empirical study, the average error rate for predicting LQ45 stock prices using ARIMA is 7,0390%, with a standard deviation of 7,7441%; for multiplying Holt-Winter, it is 29,3919%, with a standard deviation of 25,7571%; and for additive Holt-Winter, it is 18,0463%, with a standard deviation of 18,3504%. Apart from numerical comparisons, it can also be seen visually, based on the Boxplot diagram, that the MAPE of ARIMA(p,d,q) is more focused than Holt-Winter. In addition, in terms of accuracy distribution, it can be seen that the MAPE accuracy of the ARIMA method produces four outliers. Based on the MAPE accuracy rate, we conclude that Holt-Winter has a bigger error based on the MAPE value than ARIMA(p,d,q) at forecasting LQ45 stock prices.
Co-Authors Aditya Wikan Mahastama Antonius R. Chrismanto, Antonius R. Antonius Rachmat Chrismanto Argo Wibowo Astuti, Laksmita Widya Budi Susanto Budi Susanto Daniel Felix Setiawan Dody Ivana Erinda Resha Astanti F. Yogiswara Adinugraha P Ginting Pebrindanov Gloria Virginia Halim Budi Santoso Hartono, Arfiananda Dhede Hizkia Juan Suryanto Hizkia Juan Suryanto, Hizkia Juan Katon Wijana Katon Wijana Katon Wijana Kevin Valiant Kristian Hartono, Renaldi Laurentius Kuncoro Probo Saputra Laurentius Kuncoro Probo Saputra, Laurentius Kuncoro Probo Lukas Chrisantyo Lukas Chrisantyo Naibaho, Yosep Parlindungan Nugroho Agus Haryono Pranatantri, Shalommita Purwadi, Joko Raden Gunawan Santosa Ragil Yoga Irawan Restyandito Rosa Delima Sagara Mahardika Sunaryo Setiawan, Daniel Felix Susilo, Anton Valonia Inge Santoso Verianto, Eko Wahju Satria Wibowo Widi Hapsari Willy Sudiarto Raharjo