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All Journal IAES International Journal of Artificial Intelligence (IJ-AI) dCartesian: Jurnal Matematika dan Aplikasi Jurnal Sistem Komputer Proceedings of KNASTIK Journal The Winners Jurnal Teknologi Informasi dan Ilmu Komputer Scientific Journal of Informatics International Journal of Artificial Intelligence Research INTENSIF: Jurnal Ilmiah Penelitian dan Penerapan Teknologi Sistem Informasi Jurnal Penelitian Pendidikan IPA (JPPIPA) CogITo Smart Journal INOVTEK Polbeng - Seri Informatika BAREKENG: Jurnal Ilmu Matematika dan Terapan JURNAL TEKNIK INFORMATIKA DAN SISTEM INFORMASI Sebatik Jurnal ULTIMA InfoSys MATRIK : Jurnal Manajemen, Teknik Informatika, dan Rekayasa Komputer JURNAL PENDIDIKAN TAMBUSAI Digital Zone: Jurnal Teknologi Informasi dan Komunikasi JURIKOM (Jurnal Riset Komputer) JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Aptisi Transactions on Technopreneurship (ATT) Building of Informatics, Technology and Science FINANCIAL : JURNAL AKUNTANSI JUKANTI (Jurnal Pendidikan Teknologi Informasi) Jurnal Mnemonic JATI (Jurnal Mahasiswa Teknik Informatika) Jurnal Sistem Komputer dan Informatika (JSON) Community Development Journal: Jurnal Pengabdian Masyarakat Aiti: Jurnal Teknologi Informasi Jurnal Teknik Informatika (JUTIF) Advance Sustainable Science, Engineering and Technology (ASSET) International Journal of Social Science Indexia J-SAKTI (Jurnal Sains Komputer dan Informatika) KINGDOM : Jurnal Teologi dan Pendidikan Agama Kristen Jurnal Minfo Polgan (JMP) Jurnal Teknologi Sistem Informasi Jurnal Komputer Teknologi Informasi Sistem Komputer (JUKTISI) Prioritas : Jurnal Pengabdian Kepada Masyarakat Magistrorum et Scholarium: Jurnal Pengabdian Masyarakat IT-Explore: Jurnal Penerapan Teknologi Informasi dan Komunikasi Eduvest - Journal of Universal Studies Jurnal INFOTEL Journal of Technology Informatics and Engineering Jurnal Pendidikan Teknologi Informasi (JUKANTI) Jurnal Indonesia : Manajemen Informatika dan Komunikasi Scientific Journal of Informatics CSRID Society Jurnal DIMASTIK Proceedings of The International Conference on Computer Science, Engineering, Social Sciences, and Multidisciplinary Studies
Hendry
Universitas Kristen Satya Wacana
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Analisis konten budaya kolaboratif berbasis Grounded Theory menggunakan Text Mining Julians, Adhe Ronny; Manongga, Daniel Herman Fredy; Hendry, Hendry
AITI Vol 21 No 2 (2024)
Publisher : Fakultas Teknologi Informasi Universitas Kristen Satya Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24246/aiti.v21i2.230-250

Abstract

Creating a collaborative culture of innovation in an organization is very important today. A collaborative culture of innovation is not just about physically working together but also about creating an environment that supports open communication, appreciation for new ideas, and acceptance of risk. Organizations that embrace this culture can create significant added value and thrive in an ever-changing environment. This research aims to conduct a content analysis of several Grounded Theory-based reputable scientific articles using Text Mining, which involves using coding techniques to classify information and identify certain categories or codes representing certain text elements. The analysis results are a conceptual network model that connects elements that influence collaborative culture on innovation, such as Openness, Diversity, Shared Goals, Trust, Teamwork, Support, and Use of Technology. Organizations use this model to create a collaborative culture of innovation in their environment, and it can be used in further research to test the model using statistical tests.
Intelligent classification and performance prediction of multi-text assessment with recurrent neural networks-long short-term memory Paryono, Tukino; Sediyono, Eko; Hendry, Hendry; Huda, Baenil; Lia Hananto, April; Yuniar Rahman, Aviv
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp3350-3363

Abstract

The assessment document at the time of study program accreditation shows performance achievements that will have an impact on the development of the study program in the future. The description in the assessment document contains unstructured data, making it difficult to identify target indicators. Apart from that, the number of Indonesian-based assessment documents is quite large, and there has been no research on these assessment documents. Therefore, this research aims to classify and predict target indicator categories into 4 categories: deficient, enough, good, and very. Learning testing of the Indonesian language assessment sentence classification model using recurrent neural networks-long short-term memory (RNN-LSTM) using 5 layers and 3 parameters produces performance with an accuracy value of 94.24% and a loss of 10%. In the evaluation with the Adamax optimizer, it had a high level of accuracy, namely 79%, followed by stochastic gradient descent (SGD) of 78%. For the Adam optimizer, Adadelta, and root mean squared propagation (RMSProp) have an accuracy rate of 77%.
A three-step combination strategy for addressing outliers and class imbalance in software defect prediction Rizky Pribadi, Muhammad; Dwi Purnomo, Hindriyanto; Hendry, Hendry
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 13, No 3: September 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v13.i3.pp2987-2998

Abstract

Software defect prediction often involves datasets with imbalanced distributions where one or more classes are underrepresented, referred to as the minority class, while other classes are overrepresented, known as the majority class. This imbalance can hinder accurate predictions of the minority class, leading to misclassification. While the synthetic minority oversampling technique (SMOTE) is a widely used approach to address imbalanced learning data, it can inadvertently generate synthetic minority samples that resemble the majority class and are considered outliers. This study aims to enhance SMOTE by integrating it with an efficient algorithm designed to identify outliers among synthetic minority samples. The resulting method, called reduced outliers (RO)-SMOTE, is evaluated using an imbalanced dataset, and its performance is compared to that of SMOTE. RO-SMOTE first performs oversampling on the training data using SMOTE to balance the dataset. Next, it applies the mining outlier algorithm to detect and eliminate outliers. Finally, RO-SMOTE applies SMOTE again to rebalance the dataset before introducing it to the underlying classifier. The experimental results demonstrate that RO-SMOTE achieves higher accuracy, precision, recall, F1-score, and area under curve (AUC) values compared to SMOTE.
IMPLEMENTATION OF MULTI-NODE SENSOR DATA DELIVERY USING THE MASTER-SLAVE METHOD IN LORA COMMUNICATION Hendry, Hendry; Manongga, Daniel
Journal of Technology Informatics and Engineering Vol 3 No 2 (2024): Agustus : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v3i2.179

Abstract

This research explains the application of sending data from various sensor nodes using the master-slave method in Long Range (LoRa) communication. This system was created to increase efficiency and reliability in collecting sensor data spread across several locations. Sensor nodes function as slaves that collect and send data to the master. The master then processes and combines the data before sending it to a central server. Experimental results show that this method is successful in reducing latency and increasing data transmission speed and shows great potential for Internet of Things (IoT) applications that require wide communication range and low power consumption.
Analisis Sentimen E-Learning X Terhadap Antarmuka Pengguna Menggunakan Kombinasi Multinomial Naive Bayes Dan Pendekatan Design Thinking Huda, Baenil; Sembiring, Irwan; Setiawan, Iwan; Manongga, Danny; Purnomo, Hindriyanto Dwi; Hendry, Hendry; Fauzi, Ahmad; Lia Hananto, April; Tukino, Tukino
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 11 No 4: Agustus 2024
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.1147686

Abstract

Penelitian ini bertujuan untuk menganalisis sentimen pengguna terhadap antarmuka e-learning X menggunakan kombinasi Multinomial Naive Bayes dan pendekatan Design Thinking. Permasalahan yang dihadapi adalah banyaknya feedback negatif terkait antarmuka pengguna yang dianggap kurang intuitif. Data sentimen dari ulasan pengguna diklasifikasikan menggunakan algoritma Multinomial Naive Bayes, sementara Design Thinking digunakan untuk merancang solusi antarmuka yang lebih user-friendly. Hasilnya menunjukkan bahwa metode ini efektif meningkatkan sentimen positif pengguna, dengan perbaikan signifikan dalam pengalaman dan kepuasan pengguna terhadap antarmuka e-learning X, Serta rekomendasi untuk pengembangan aplikasi e-learning.   Abstract   This research aims to analyze user sentiment towards the e-learning interface X using a combination of Multinomial Naive Bayes and Design Thinking approaches. The problem faced was the large number of negative feedback regarding the user interface which was considered less intuitive. Sentiment data from user reviews is classified using the Multinomial Naive Bayes algorithm, while Design Thinking is used to design more user-friendly interface solutions. The results show that this method is effective in increasing positive user sentiment, with significant improvements in user experience and satisfaction with the X e-learning interface As well as recommendations for developing e-learning applications.
ANALISIS SISTEM LAYANAN PENDAFTARAN E-KTP MENGGUNAKAN FRAMEWROK FOR THE APPLICATION OF SYSTEM THINKING Ronny Julians, Adhe; Manongga, Danny; Hendry, Hendry
JATI (Jurnal Mahasiswa Teknik Informatika) Vol. 7 No. 1 (2023): JATI Vol. 7 No. 1
Publisher : Institut Teknologi Nasional Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36040/jati.v7i1.6393

Abstract

Pelayanan terkait pembuatan e-KTP pada Dinas Kependudukan dan Pencatatan Sipil Kabupaten Mimika, hingga saat ini masih sering ditemui berbagai permasalahan didalam prosesnya, dimulai dari jauhnya jarak tempuh ke kantor terkait yang memakan waktu serta biaya yang tidak sedikit, proses pendaftaran yang lama karena antrian yang panjang, serta adanya penumpukan data yang mengakibatkan pelayanan menjadi tidak efisien dalam penggunaan waktu. Terkait dengan hal tersebut, maka tujuan dari penelitian ini adalah untuk memberikan solusi mengenai bagaiamana membuat model perancangan sistem layanan pendaftaran e-KTP berbasis web dengan menggunakan Framework For The Application Of System Thinking sebagai pendekatan dalam penyusunan penelitian, yang diharapkan dapat mempermudah didalam proses pendaftaran terkait dengan layanan e-KTP yang cepat, mudah, ramah, gratis dan mudah dijangkau.
Analisis Perbandingan Algoritma Supervised Learning untuk Prediksi Kasus Covid-19 di Jakarta Septhiani, Angeline; Hendry, H
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.668

Abstract

Coronavirus disease or called COVID-19 is a pandemic according to World Health Organization (WHO) in February. The virus gives several symptoms, such as cough, asthma, and fever. The data and information are the important part of making a good decision. Those data need to be processed and analyzed to be useful information. In this research, the data will be used to predict the COVID-19 issue in Jakarta, using several supervised learning algorithm models, such as K-Nearest Neighbors, Neural Network, Linear Regression, Support Vector Machine, and Random Forest. Using 10 Fold Cross Validation in model testing and T-Test to get the model with the best accuracy. According to this research, the algorithm that has the best accuracy is K-Nearest Neighbors with the lowest RMSE, 1096.188 +/- 365.077 (micro average: 1149.601 +/- 0.000).
Perbandingan Metode SAW, MAUT, ORESTE, TOPSIS dalam Pendukung Keputusan Pembangunan Supermarket di Kabupaten Pati Dewasasmita, Elsha Yuandini; Hendry, H
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 7, No 2 (2023): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v7i2.666

Abstract

This study aims to find out the best sub-districts in Pati Regency which are located outside Pati District as a place for Supermarket construction based on the specified criteria and a comparison of the four methods to be used. The tool used to support this research process is Microsoft Excel. This study uses the SAW, MAUT, ORESTE, and TOPSIS methods in the research model to compare the final results. The final results obtained are that the SAW and TOPSIS methods have the first three orders, namely A10, A15, and A3, the MAUT method has the same first three orders, namely A10, A3, and A15, while the ORESTE method has the first three orders, namely A21, A10, and A3. By looking at the opportunities for emergence, the final results show A10, namely Kayen District as the best sub-district in supporting supermarket development decisions in Pati Regency.
Predicting Transjakarta Passengers with LSTM-BiLSTM Deep Learning Models for Smart Transportpreneurship Siswanto, Joko; Hendry, Hendry; Rahardja, Untung; Sembiring, Irwan; Lisangan, Erick Alfons
Aptisi Transactions On Technopreneurship (ATT) Vol 7 No 1 (2025): March
Publisher : Pandawan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.34306/att.v7i1.440

Abstract

Travel pattern variations pose challenges in building a prediction model that accurately captures seasonal patterns or precision of BRT passenger numbers. An approach that integrates sophisticated prediction algorithms with high accuracy is needed to address the Transjakarta BRT passenger number prediction model problem. The proposed prediction model with the best accuracy is sought using deep learning on 8 models. The prediction model is used for short-term and long-term predictions, as well as looking for correlations in the prediction results of 13 Transjakarta corridors. The Python programming language with the Deep Learning Tensor Flow framework is run by Google Colaboratory used in the prediction simulation environment. The combination of BiLSTM-CNN was found to have the best accuracy of the evaluation value (SMAPE = 15.9387, MAPE = 0.598, and MSLE = 0.0425), although it has the longest time (134 seconds). Fluctuations in short-term predictions of passenger numbers evenly occur simultaneously across all corridors. Fluctuations in long-term predictions evenly occur simultaneously across all corridors, except in February. There is no negative correlation in the 13 prediction results and there are 8 corridors that have a close positive correlation. The prediction results can be used by transportation operators and the government to optimize resource planning and transportation policies to support sustainable community and economic mobility.
PERFORMANCE ANALYSIS OF GRADIENT BOOSTING MODELS VARIANTS IN PREDICTING THE DIRECTION OF STOCK CLOSING PRICES ON THE INDONESIA STOCK EXCHANGE Kho, Delvian Christoper; Purnomo, Hindriyanto Dwi; Hendry, Hendry
BAREKENG: Jurnal Ilmu Matematika dan Terapan Vol 19 No 2 (2025): BAREKENG: Journal of Mathematics and Its Application
Publisher : PATTIMURA UNIVERSITY

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30598/barekengvol19iss2pp1393-1408

Abstract

Accurately predicting stock market trends remains a significant challenge for investors due to its dynamic nature. This study explores the performance of Gradient Boosting models, including XGBoost, XGBoost Random Forest, CatBoost, and Gradient Boosting Scikit-Learn, in predicting stock market trends such as sideways movement, uptrends, downtrends, and volatility. Using four datasets from the Indonesia Stock Exchange, the research integrates technical, fundamental, and sentiment data, encompassing 37 features. Modeling and testing are conducted using Orange tools and Python, with performance evaluated through metrics such as Mean Absolute Percentage Error (MAPE), R-squared (R²), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Results indicate that XGBoost and XGBoost Random Forest consistently outperform other models in predicting stock price movements. These findings highlight the potential of Gradient Boosting models in providing accurate and reliable predictions, offering valuable insights for investors, financial analysts, and researchers to enhance investment strategies and adapt to market fluctuations effectively.
Co-Authors Ade Iriani Adenia Kusuma Dayanthi Adriyanto Juliastomo Gundo Agista Nindy Yuliarina Agus Susanto Amanda, M. F. Anton Hermawan April Lia Hananto Atik Setyanti, Angela Aviv Yuniar Rahman Baihaqi, Kiki Ahmad Benedictus Lanang Ido Hernanto Christine Dewi Daniel, Benny Danny Manongga Darwin Lie Dewasasmita, Elsha Yuandini Dewi Puspitasari Eka, Muhammad Eko Sediyono eric secada purba Erick Alfons Lisangan Erits Talapessy Erwien Christianto Ester Caroline Dwi Wijaya Wijaya Fauzi Ahmad Muda Fenny Fenny Franly Salmon Pattiiha Fredryc Joshua Pa'o Giarti, Giarti Gunawan, Ricardho Handoko Handoko Handoko, Andrew C Hanita Yulia Hendra Waskita Herdin Yohnes Madawara Hindriyanto Dwi Purnomo Huda, Baenil Indriaty, Novica Irwan Sembiring Ismael Ismael Ivan Sukma Hanindria Iwan Setiawan Iwan Setyawan Jessica Margaret Br Sembiring Joko Siswanto Julians, Adhe Ronny Kesumawati, Ramadini Kho, Delvian Christoper Krismiyati Kristoko Dwi Hartomo Kurnia, Sri Kurniawan Teguh Martono Leni Marlina Liawatimena, S. Lidia Gayatri Madawara, Herdin Yohnes Mado, Priscianus Mikael Kia Magda Kitty Hartono Mahulete, Ebenhaezer Yohanes Abdeel Manongga, Daniel Margaretha Intan Pratiwi Hant Martaliana Putri Agustina Merryana Lestari Muhammad Khahfi Zuhanda Muhammad Rizky Pribadi Nadia Sofie Soraya Nalbraint Wattimena Nansy Stephanie Mongi Novrina, Putri Dwi Nugraha, Febrina Tesalonika Panja, Eben Paryono, Tukino Pratama Siregar, Hari Nanda Pratama, Arya Damar Purnomo, Hendryanto Dwi Raden Mohamad Herdian Bhakti Ramos Somya Ravensca Matatula Ravensca Matatula Richard V. Llewelyn Rizal, Chairul Robertus Bagaskara Radite Putra Ronny Julians, Adhe Rostina, Cut Fitri Rung Ching Chen Santoso, Joseph Teguh Saputri, Adelliya Dewi Septhiani, Angeline Sholikin, Muhammad Simanjuntak, Dahnil Anzar Sjukun Suharyadi Suherman, Suherman Sukiman Sukiman Supiyandi Supiyandi Susanta, Vonny A. Sutarto Wijono Suvirocana, Suvirocana Syefudin Syefudin Tarigan, Aldi Ekin Arapenta Teddy Marcus Zakaria Theopillus J. H. Wellem Tukino, Tukino Uly, Novem Untung Rahardja Vanisa Meifari Wahyuningsih, Novia Wibowo, Kurniawan Indra Widi, Anugerah Wijaya, Elyzabeth Winny purbaratri Yandra Rivaldo Yessica Nataliani Zulham Zulham