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All Journal Teika Jurnal Sarjana Teknik Informatika Scan : Jurnal Teknologi Informasi dan Komunikasi Teknika: Jurnal Sains dan Teknologi Jurnal IPTEK Jurnal Sistem Informasi dan Bisnis Cerdas Journal of Information System JOIV : International Journal on Informatics Visualization Jurnal Pendidikan Informatika dan Sains INTEGER: Journal of Information Technology JURNAL ILMIAH INFORMATIKA Jurnal Informatika Universitas Pamulang RESEARCH : Computer, Information System & Technology Management Jiko (Jurnal Informatika dan komputer) Jurnal Ilmiah Sinus Jurnal Ilmiah Ilmu Komputer Fakultas Ilmu Komputer Universitas Al Asyariah Mandar JUTIS : Jurnal Teknik Informatika Jurnal Teknologi Informasi dan Terapan (J-TIT) bit-Tech Jurnal Informatika dan Rekayasa Perangkat Lunak Jurnal Teknologi Informasi : Jurnal Keilmuan dan Aplikasi Bidang Teknik Informatika Jifosi e-NARODROID Nusantara Science and Technology Proceedings Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer) Journal of Technology and Informatics (JoTI) Jurnal Informatika Teknologi dan Sains (Jinteks) Jurnal Penelitian Sistem Informasi Nusantara Journal of Computers and its Applications Jurnal Informatika Polinema (JIP) Jurnal Pengabdian Teknik dan Sains (JPTS) Prosiding Seminar Nasional Ilmu Sosial dan Teknologi (SNISTEK) JuTISI (Jurnal Teknik Informatika dan Sistem Informasi) Jurnal Serumpun Teknik Informatika
Parlika, Rizky
Universitas Pembangunan Nasional Veteran Jawa Timur
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Pengukuran Validitas Website “mengundangkamu.online” Menggunakan SPSS dengan Kombinasi Metode R-Table dan Coehn’s Cappa Parlika, Rizky; Shahab, Muhammad Syaugi; Nurilhaq, Muhammad Sabilli; Firmansyah, Fahrul; Prayoga, Julio Cahya
JURNAL TEKNIK INFORMATIKA UNIS Vol. 10 No. 1 (2022): Jutis (Jurnal Teknik Informatika)
Publisher : Universitas Islam Syekh Yusuf

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33592/jutis.v10i1.2624

Abstract

In this study, the validity test was carried out on the website "inviting you.online" by calculating the validity value of the respondents' answers to the questionnaire to determine the level of operational feasibility of the website. Meanwhile, the questions from the questionnaire are the attributes contained on the website with a sample of 30 students and college students in the East Java region randomly and when the research was conducted in July 2022. From the results of processing the questionnaire data using SPSS by applying the method R-Table and the combination of Coehn's Cappa, then the R-Table method obtained the percentage of validity of 100% and invalidity of 0%. Then, with the combination of the Coehn's Cappa method, a value of 0.737 was obtained with a significant value of 0.016, which is where the level of strength of the appraiser's agreement on the website is moderate.
Perancangan Sistem Informasi Jual Beli Ikan Cupang Berbasis Website Auliya, Rahmat; Prabowo, Avrie Akbar; Parlika, Rizky; Shodiq, Ja'far; Waskito, Muhammad Rizal
Jurnal Sarjana Teknik Informatika Vol. 9 No. 3 (2021): Oktober
Publisher : Program Studi Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v9i3.19553

Abstract

Dalam masa sekarang ini, kita dituntut untuk mengikuti arus perkembangan teknologi yang berkembang sangat cepat. Hal ini berlaku juga dalam perdaga-ngan. Maraknya Perdangan ikan cupang yang terus meluas mengharuskan penjualannya tidak hanya sekadar melalu offline tetapi juga secara online. Pembuatan Website ini sendiri memanfaatkan bahasa pemrograman PHP yang dibuat menggunakan aplikasi editor Visual Studio Code dalam pengem-bangannya yang nantinya akan digunakan untuk wadah dari keseluruhan data untuk penunjang berjalannya penjualan ikan cupang. Mulai dari pemesanan hingga total pemesanan akan diimplementasikan ke dalam sebuah Website.
Pemanfaatan Canva Untuk Kebutuhan Desain Grafis dan Video Promosi Edotel TeBe Syariah Andreas Nugroho Sihananto; Kartini Kartini; Rizky Parlika
Prosiding Vol 4 (2022): SNISTEK
Publisher : LPPM Universitas Putera Batam

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

Abstract

Marketing activities are at the core of every business, even in hospitality business. The hotels today is being challenged in marketing, especially digital marketing, and one of them is Edotel TeBe Syariah which is owned by SMK Tunas Bangsa on Malang City. The challenge faced by Edotel is the lack of human resources who master visual content creation and video creation for digital marketing purposes. Although it has confirmed business cooperation with OYO as SPOT ON 90349 for its marketing channel, the management also wants to strengthen its digital marketing side through social media. Because of the Covid-19 pandemic, the hotel occupancy rate is uncertain, sometimes only a few rooms are filled. The management also wants to increase the capacity of their human resources. our community service team and management plan marketing teaching strategies using Canva-based visual and video content. After using the Canva application for a while, all permanent employees of Edotel TeBe Syariah are now able to use Canva to create visual and video content for marketing needs. We hope that digital marketing activities through social media such as Facebook and Instagram owned by Edotel TeBe Syariah can be further improved
Pengembangan Website untuk Menampilkan Harga Koin Kripto dengan Antarmuka Pemrograman Aplikasi Rizky Parlika; Achmad Yuneda Alfajr; Alif Ernanda Putra; Ahmad Dendy Prasongko Putra
Jurnal Teknik Informatika dan Sistem Informasi Vol 8 No 3 (2022): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v8i3.4285

Abstract

Cryptocurrency is one of the investment instruments and trading assets chosen by old and new traders today. Lots of people claim that the information from the platforms they use is just numbers or trivial information. However, many ordinary people who really need it are still confused about this information. In this study, an application programming interface was developed to help new traders using the instrument easier.
Prediction of ROI Achievements and Potential Maximum Profit on Spot Bitcoin Rupiah Trading Using K-means Clustering and Patterned Dataset Model Parlika, Rizky; Isnanto, R. Rizal; Rahmat, Basuki
JOIV : International Journal on Informatics Visualization Vol 8, No 3-2 (2024): IT for Global Goals: Building a Sustainable Tomorrow
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3-2.3120

Abstract

Since Satoshi Nakamoto first proposed the idea of bitcoin in 2009, the cryptocurrency and prediction methods for it have grown and changed exceptionally quickly. The Patterned Dataset Model was a valuable tool in earlier studies to explain how changes in the price of Bitcoin affect the movements of other cryptocurrencies in a digital trading market. Three different kinds of datasets are generated by this model: patterned datasets under full conditions, patterned datasets under dropping prices (Crash), and patterned datasets under rising prices (Moon). The K-means approach was then used to cluster these three datasets. Specifically, each dataset was split into two clusters, and the clustering score was determined by utilizing eight unique clustering metrics. Consequently, the best clustering score was found in the patterned dataset in the crash situation. Additionally, from 2022 to 2024, the raw data from this crash-condition-patterned dataset is used to determine the possibility of reaching maximum profit and return on investment (ROI) daily and monthly. According to the calculation results, the range computed over the course of a whole month (30 to 31 days) is significantly larger than the daily range (24 hours multiplied by one month), which represents the most significant profit and ROI attained before the emergence of the first diamond crash level. This research also covers the application of a deep learning model to forecast patterned datasets for crash scenarios that may occur many days in advance. The ConvLSTM2D Model performs better in predicting pattern dataset values for the subsequent crash scenario, according to the hyperparameter comparison between the Gated Recurrent Unit (GRU) Model and the 2D Convolutional Long Short-Term Memory Model.
Minimum, Maximum, and Average Implementation of Patterned Datasets in Mapping Cryptocurrency Fluctuation Patterns Parlika, Rizky; Mustafid, Mustafid; Rahmat, Basuki
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.1543

Abstract

Cryptocurrency price fluctuations are increasingly interesting and are of concern to researchers around the world. Many ways have been proposed to predict the next price, whether it will go up or down. This research shows how to create a patterned dataset from an API connection shared by Indonesia's leading digital currency market, Indodax. From the data on the movement of all cryptocurrencies, the lowest price variable is taken for 24 hours, the latest price, the highest price for 24 hours, and the time of price movement, which is then programmed into a pattern dataset. This patterned dataset is then mined and stored continuously on the MySQL Server DBMS on the hosting service. The patterned dataset is then separated per month, and the data per day is calculated. The minimum, maximum, and average functions are then applied to form a graph that displays paired lines of the movement of the patterned dataset in Crash and Moon conditions. From the observations, the Patterned Graphical Pair dataset using the Average function provides the best potential for predicting future cryptocurrency price fluctuations with the Bitcoin case study. The novelty of this research is the development of patterned datasets for predicting cryptocurrency fluctuations based on the influence of bitcoin price movements on all currencies in the cryptocurrency trading market. This research also proved the truth of hypotheses a and b related to the start and end of fluctuations.
Patterned Dataset Model Optimization to Predict Bitcoin IDR Price using Long Short Term Memory Parlika, Rizky; Isnanto, R Rizal; Rahmat, Basuki
JOIV : International Journal on Informatics Visualization Vol 9, No 6 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.6.4036

Abstract

The goal of this study was to determine the optimal combination for optimizing the Patterned Dataset Model, particularly in patterned datasets during periods of price decline (crash).  In previous research, the Crash Patterned Dataset has been shown to predict the next Bitcoin price. In this study, an experiment was conducted using a combination of prediction models, including ARIMA, machine learning, and deep learning. This research was conducted in 3 stages. The first stage is to compare the error results from the Bitcoin pair IDR crypto asset prediction process, which are part of the stored data from the patterned dataset under crash conditions. This dataset was tested with several prediction models, and the LSTM model with 60 seconds of resampling produced the best results, with an MAPE of 0.19%. In the second stage, BTCIDR, as part of the data from the patterned dataset in crash conditions, was resampled with variants 1D, 2D, 3D, 4D, 5D, 6D, 7D, 1H, 2H, 3H, 4H, 5H, 6H, 7H, 8H, 9H, 10H, 11H, and 12H. The result is that BTCIDR with a 3H resample has the lowest MAPE, at 1.3%. In the third stage, the prediction process is carried out using the LSTM model on the BTC IDR test dataset (as part of the Patterned Dataset in crash conditions) with a 3H resample. The dataset range is from May 2022 to 2025-01-23 11:05:48. This test predicts the Bitcoin IDR price series for the next 30 days, calculates the MAPE between the predicted series and the actual BTC IDR dataset 30 days later, and evaluates the results. The MAPE value for the Bitcoin IDR price prediction was 9.27%. This indicates that the average prediction error against the actual price is around 9.27%. The main objective of this research is to more accurately predict the price of the Bitcoin-IDR pair, providing additional helpful information for trading cryptocurrencies.
Analisis Website Pusat Pengembangan Karir Berdasarkan Pengalaman Pengguna: Pendekatan User Experience Questionnaire (UEQ) Aris Pratama; Siti Mukaromah; Mohammad Idhom; Asif Faroqi; Tri Lathif Mardi Suryanto; Rizky Parlika
Jurnal Teknologi Informasi dan Terapan Vol 10 No 2 (2023)
Publisher : Jurusan Teknologi Informasi Politeknik Negeri Jember

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25047/jtit.v10i2.321

Abstract

Pembangunan National “Veteran” University of East Java has developed the Career Development Center website as a job vacancy information center to prepare graduates who are tough, innovative, and creative in the world of work. The Career Development Center website makes it easier to search for and find job opportunities that suit the field of study, skills, and interests of college graduates. A positive user experience in using the Career Development Center website can increase satisfaction and optimize career searches, so it is important to carry out measurements systematically and objectively. This study aims to measure the level of experience of users of the Career Development Center website using the User Experience Questionnaire (UEQ). UEQ measurements on the Career Development Center website focus on gaining an understanding of the Attractiveness, Pragmatic quality, and Hedonic quality aspects. UEQ provides measurements of technical and non-technical aspects related to the user's emotion or perception of pleasure. The results of measuring user experience on the Career Development Center website can help identify trends and changes in user needs so that they can adapt and develop relevant and innovative features. This study uses quantitative data by distributing 26 items of the UEQ questionnaire online to 350 alumni. The research results stated that the attractiveness aspect had the highest average value of 1.94. These results prove that the Career Development Center website has a high attractiveness value for users, thereby increasing the opportunity for continuous use. Apart from that, based on the high average stimulation value of 1.930, it can be proven that graduates who use the Career Development Center website get large benefits related to alumni career development.
Perbandingan Fuzzy Mamdani dan Sugeno dalam Optimasi Trading Bitcoin Berbasis Indikator Teknikal Dwi Rahmadewi, Cynthia; Parlika, Rizky; Maulana, Hendra
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11235

Abstract

This study compares Mamdani and Sugeno fuzzy inference systems for Bitcoin trading using historical BTC/USDT data. In highly volatile and non-linear cryptocurrency markets, especially during bear markets, conventional methods struggle to interpret ambiguous signals, making fuzzy logic suitable for adaptive decision-making. The dataset was collected from the Binance API for the period 20 November 2021 to 31 December 2022 and consists of 9,746 candlestick records. This period corresponds to a bear market phase, characterized by a significant downward trend in Bitcoin prices, which provides a challenging environment for evaluating trading strategies. Four technical indicators, Bollinger Bands, RSI, ADX, and PSAR, were used as input variables. The data were split into 70% training and 30% testing using a time-based approach. Performance evaluation was conducted through long-only backtesting using Total Profit, Win Rate, Maximum Drawdown, Sharpe Ratio, and Sortino Ratio. The results show that Mamdani achieved better profitability than Sugeno, with total profit of -34.17% on training data and -2.45% on testing data, while Sugeno produced -53.91% and -3.04%, respectively. Although both methods resulted in negative returns due to the bearish market conditions, their performance was better than the buy-and-hold strategy, which recorded losses of -65.78% on training data and -17.49% on testing data. This indicates that both fuzzy approaches were effective in reducing losses and improving risk management under extreme market conditions. However, Sugeno showed better risk control on testing data with a lower maximum drawdown of 18.72% compared to 25.01% for Mamdani. Overall, Mamdani is more suitable for return-oriented strategies, while Sugeno is more appropriate for risk management under bearish conditions.
Klasifikasi Rating Film Berdasarkan Genre Menggunakan XGBoost dan LightGBM serta Analisis SHAP Roiqoh, Aprinia Salsabila; Parlika, Rizky; Aditiawan, Firza Prima
JURNAL FASILKOM Vol. 16 No. 1 (2026): Jurnal FASILKOM (teknologi inFormASi dan ILmu KOMputer)
Publisher : Unversitas Muhammadiyah Riau

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37859/jf.v16i1.11273

Abstract

Movie rating is often used as an indicator of film quality and audience satisfaction. With the large availability of movie data on online platforms, machine learning techniques can be used to analyze the relationship between film characteristics and rating patterns. One important attribute that can influence movie ratings is genre. This study aims to classify movie ratings based on genre using the XGBoost and LightGBM algorithms and to analyze the contribution of each genre using SHAP (SHapley Additive Explanations). Movie data were collected from The Movie Database (TMDB) API and processed through several preprocessing stages including genre separation, data cleaning, one-hot encoding, and rating categorization. The dataset was then divided into training and testing data with a ratio of 70:30. The classification results show that XGBoost achieved an accuracy of 0.53, slightly higher than LightGBM with an accuracy of 0.52. Further analysis using SHAP indicates that genres such as Horror, Drama, Action, and Comedy have the highest global importance in the classification model. Meanwhile, the analysis of high-rating class predictions shows that Drama has the largest contribution to predicting movies with high ratings. The findings indicate that movie genres have a measurable influence on rating classification, although the importance of genres in the machine learning model does not always align with their average rating values.
Co-Authors A. Kachsyfur Djasim Ilyas Paenrongi Abidin Sulaiman Abrori, Merdin Risalul Achmad Heidhar Mubarok, Achmad Heidhar Achmad Yuneda Alfajr Agussalim Ahmad Budi Trisnawan Ahmad Budi Trisnawan Ahmad Dendy Prasongko Putra Ahmad Maghfur’ Ali Akbar, Fawwaz Ali Akhlis Munazilin Akhlis Munazilin Akhlis Munazilin, Akhlis al hakim, Rais Alfajr, Achmad Yuneda Alif Ernanda Putra Alwin, Muhammad Izdihar Amir Muhammad Hakim Andreas Nugroho Sihananto Andry S, Firdaus Anggoro Cahyo Nugroho Anggreini, Diana Nur Anindya Khalisha Nurdianti Anita Nusari Ardiana Deka Maharani Ardisty Palvelus Jumala Arianto, Chakra Satrya Pradana Putra Arif Nur Cahyo Aris Pratama Arista Pratama Asif Faroqi Atmaja, Pratama Wirya Aulia N, Rayhan Auliya, Rahmat Avrie Akbar Prabowo Ayu Ithriah, Syurfah Basuki Rahmat Masdi Siduppa Benny Danendra Hadi Bima Rizqy Prasurya Bregsi Atingsari Julastri Bryan Benedict Bangun Caritta Elizabeth Chakra Satrya Pradana Putra Arianto Choirun Nisa Dandi Azaidane Devan Cakra Mudra Wijaya Devi Anugrah Putri Dewi Azizah Dhany Satya Hutama Didik U Pribadi, Didik U Dimara Kusuma Hakim Dimas Rizward Hikmah Utomo Dino Rosanilo Yuswanto Dwi Rahmadewi, Cynthia Eka Maurita Maurita Emmil Yulianto Erayanti, Aninda Elsa Fadhli Shidqi Wiratama faradilla, yolla Faris Al Fatih, Mohammad Faris Hirmar Pralas Fatwa Zuhri Diva Perdana Fedianto, Muhammad Helmi Satria Fernanda, Rifky Akhmad Fernanda, Rifky Akhmad Firmansyah, Fahrul Firza Prima Aditiawan Fitranda Ramadhana Gayuh Abdi Mahardika Hadiansyah Rachmawan Putra Haidar Ananta Kusuma Hakim, Arif Rahman Hamdi Indra Hanafi, Agus Heldian Lintang P Heri Khariono Heri Khariono Hidayat Nur Tauhid Hidayat, Mochammad Fikri Hilman Fadlilah Lesmana Humam Maulana Tsubasanofa Ramadhan Humam Maulana Tsubasanofa Ramadhan Humania B, Nobel Ilham Asy’ari Ilham Krisdianta Siregar Ilham Pradika, Sunu Ilham Setia R Isfan Rachmad Ja'far Shodiq Jerry Ramadhani Cahyas Kartini Kartini Kartini Kartini Kemal Fahreza Jibran Jibran Khariono, Heri Kholilul Rachman Nur Manab Lendy Rahmadi Lesmana, Hilman Fadlilah Lintang P, Heldian Luthfiyatul ‘Azizah M. Muharrom Al Haromainy M. Syahrul Munir, M. Syahrul M. Zaky Pria Maulana Malik, Gamar Ramadhani Maulana, Hendra Melinda Shilatil Fauziyah Merdin Risalul Abrori Miftakhoneki, Sufi Misbahul Munir Mochammad Fikri Hidayat Mochammad Zayyan Ramadhan Moh. Ainur Rofik Mohammad Idhom Muhamad Faizhal Musthafa Muhammad Agung Shobirin Muhammad Ghifari Alifian Muhammad Hakim, Amir Muhammad Helmi Satria Fedianto Muhammad Ilham Arzaki Muhammad Izdihar Alwin Muhammad Rafli Aulia Rojani Lutfi Muhammad Rizal Waskito Muhammad Romi Nasution Muhammad Suriansyah Muhammad Wifaqul Azmi Munir, M Syahrul Mustafid Mustafid Nafa Nabila El Indri Nizam, M Miftahul Nobel Humania B Nugroho, Budi Nur Manab, Kholilul Rachman Nurilhaq, Muhammad Sabilli Olivia i Anggun Permatasar Orissa, Dendy Fektor Parlika, Anjaya Perdana, Fatwa Zuhri Diva Prabowo, Avrie Akbar Pralas, Faris Hirmar Prasasti Karunia Farista Ananto Prasasti Karunia Farista Ananto Prayoga, Julio Cahya Pribadi, Didik U Pribadi, Didik Utomo Putra Dwi Wira Gardha Yuniahans Putra, Ahmad Dendy Prasongko Putra, Alif Ernanda Putra, Hadiansyah Rachmawan Qonitah Jihan Nabilah R Rizal Isnanto R. Rizal Isnanto Rachman N.M., Kholilul Rahmat Auliya Ramadhan, Ferry Dzaky Rayhan Aulia N Rayhan Rizal Mahendra Rayhan Saneval Arhinza Rendra Ardika Retno Mumpuni Reza Achmad Gallanta Rifardi Taufiq Yufananda Rifky Akhmad Fernanda Rifky Akhmad Fernanda Riky Hermawan Riky Hermawan Rivaldy Setiawan, Rivaldy Rizky Ananda Ramadhan Rizqy Khoirul Waritsin Roiqoh, Aprinia Salsabila S. Gama, Nemicio de Salsa Pramudhita Agustiardani Sarirotul Latifah Setia R, Ilham Setiawan, Rienaldi Shahab, Muhammad Syaugi Shodiq, Ja'far Siregar, Ilham Krisdianta Steffanuel Pranatalie Krispriyanto Stevanus Frangky Handono Sunu Ilham Pradika Suriansyah, Muhammad Susy Rahmawati Syafriansyah, Muhammad Syahrul Munir Syaiful Hidayat, Syaiful Syalum Marsya Pruista Tasya Ardhian Nisaa’ Tegar Satria Kirana Trisnawan, Ahmad Budi Ummam, Mohamad Arel Intidhofatul Vinza Hedi Satria Vito Fausta Majid Wahyu Syaifullah Jauharis Saputra Wahyudi Wahyudi Waskito, Muhammad Rizal Wigananda Firdaus Putra Aditya Wijaya, Devan Cakra Mudra Wijaya, Devan Cakra Mudra Yoga Ari Tofan Yudhistira Nanda Kumala Yulianto, Emmil