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All Journal Scientific Journal of Informatics Prosiding SI MaNIs (Seminar Nasional Integrasi Matematika dan Nilai-Nilai Islami) JOIV : International Journal on Informatics Visualization Creative Information Technology Journal Techno Nusa Mandiri : Journal of Computing and Information Technology JURNAL ILMIAH INFORMATIKA JurTI (JURNAL TEKNOLOGI INFORMASI) Informatik : Jurnal Ilmu Komputer CSRID (Computer Science Research and Its Development Journal) Jurnal Teknologi Informasi dan Terapan (J-TIT) Informatika Indonesian Journal of Business Intelligence (IJUBI) IJIIS: International Journal of Informatics and Information Systems BERNAS: Jurnal Pengabdian Kepada Masyarakat Journal of Applied Data Sciences International Journal of Computer and Information System (IJCIS) Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD International Journal of Robotics and Control Systems Jurnal Pengabdian Masyarakat IPTEK Jurnal Ilmiah Edutic : Pendidikan dan Informatika SAGA: Journal of Technology and Information Systems Bangun Desa : Jurnal Pengabdian Masyarakat Jurnal Medika: Medika Journal of Intelligent Systems and Information Technology Jurnal Sistem Informasi dan Teknologi Informasi Inovate : Jurnal Ilmiah Inovasi Teknologi Informasi KOMPUTASI
Pujo Hari Saputro
Sam Ratulangi University
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Chemical Engineering, Chemistry & Bioengineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Library & Information Science Mathematics Medicine & Pharmacology Neuroscience Nursing Public Health Social Sciences Veterinary Other

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Implementasi Algoritma Exclusive OR (XOR) Dalam Pengembangan Aplikasi Chat Berbasis Android Pujo Hari Saputro
Jurnal Sistem Informasi dan Teknologi Informasi Vol 2 No 1 (2023): Jurnal Sistem Informasi dan Teknologi Informasi
Publisher : Himpunan Penggiat Teknologi Informasi Abrar Indonesia

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Abstract

Android is an operating system that is widely used in this modern era. This is inseparable from Android features which are increasingly being developed to facilitate the user experience. Along with these developments, security issues also arise when exchanging information or data. Security is an important aspect of the development of the modern information technology era, so that a branch of knowledge that studies data security or known as cryptography develops. This study aims to design and build a secure chat application based on Android and implement the XOR algorithm. This algorithm is used to secure information so that it cannot be read before it reaches the recipient. This algorithm calculation produces a random message (ciphertext) that can only be read by the recipient. The result of this search is a chat application in which there is a security feature where the recipient must enter the same key as the sender so that the message can be read. Testing this application is done by sending a message and then entering the same key between the sender and the recipient
Penerapan GTMetrix Dan K6 Dalam Pengujian Performa Dan Tingkat Stress Pada Website POS (Point Of Sale) (Studi Kasus Website waroeng99 ) Pujo Hari Saputro
Jurnal Sistem Informasi dan Teknologi Informasi Vol 2 No 1 (2023): Jurnal Sistem Informasi dan Teknologi Informasi
Publisher : Himpunan Penggiat Teknologi Informasi Abrar Indonesia

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Abstract

The development of technology which continues to increase day by day has an impact in various fields, especially in the field of sales. It has been proven that there have been many developments in sales services or services carried out online (E-commerce). in this case, of course, requires an application to process data and transactions, one of which is "waroeng99". waroeng99 is a POS (point of sale) website that is used for management and recording buying and selling transactions online. With the increase in the use of services on this website, a test was carried out using the GTMetrix and LoadImpact (K6) websites to test the performance and stress levels on the website. So that you can find several problems on the waroeng99 website.
Exploratory Data Analysis & Booking Cancelation Prediction on Hotel Booking Demands Datasets Saputro, Pujo Hari; Nanang, Herlino
Journal of Applied Data Sciences Vol 2, No 1: JANUARY 2021
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v2i1.20

Abstract

Online ordering is the latest breakthrough in the hospitality industry, but when it comes to booking cancellations, it has a negative impact on it. To reduce and anticipate an increase in the number of booking cancellations, we developed a booking cancellations prediction model using machine learning interpretable algorithms for hotels. Both models used Random Forest and the Extra Tree Classifier share the highest precision ratios, Random Forest on the other hand has the highest recall ratio, this model predicted 79% of actual positive observations. These results prove that it is possible to predict booking cancellations with high accuracy. These results can also help hotel owners or hotel managers to predict better predictions, improve cancellation regulations, and create new tactics in business.
Comparison of Apriori Algorithm and FP-Growth in Managing Store Transaction Data Anas, Syukron; Rumui, Nelson; Roy, Andi; Saputro, Pujo Hari
International Journal of Computer and Information System (IJCIS) Vol 3, No 4 (2022): IJCIS : Vol 3 - Issue 4 - 2022
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v3i4.96

Abstract

The role and position of data in today's digital era are very important, data can be likened to a resource that can be explored further to produce new information or knowledge. Seeing the importance of data position, several solutions can be offered in getting more value from data, one of which is the use of Data Mining techniques with association techniques, several types of association techniques are a priori algorithms and FP-Growth algorithms. Based on the research results, the a priori algorithm produces a combination of goods with a confidence value of 98.4 and a support value of 98.4, and the algorithm produces a combination of goods with a support value of 95.2 and a confidence value of 95.2. The comparison of these two algorithms in making associations results in a faster execution time of the FP-Growth algorithm than Apriori, and the Apriori algorithm produces more varied itemset combinations.
UNVEILING GENDER FROM INDONESIAN NAMES USING RANDOM FOREST AND LOGISTIC REGRESSION ALGORITHMS Pradana, Musthofa Galih; Saputro, Pujo Hari; Tyas, Dyah Listianing
Jurnal Techno Nusa Mandiri Vol. 21 No. 2 (2024): Techno Nusa Mandiri : Journal of Computing and Information Technology Period o
Publisher : Lembaga Penelitian dan Pengabdian Pada Masyarakat

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/techno.v21i2.5537

Abstract

Gender detection can be done in many ways, some of these ways by using image identification such as the process of image identification based on faces or image shapes, on the other hand image identification and detection can also be done based on text or written data. The usefulness of gender identification can be used in various aspects of life, ranging from greetings such as ladies and gentlemen, which will certainly make the person concerned feel more appreciated by the accuracy of the pronunciation of the name. This gender identification and detection process can be done by making class predictions on predetermined gender label classes. Of course, each name in various languages has different characteristics in identifying and representing each gender, as well as Indonesian names that have diversity and unique levels of variation. The purpose of this study is to test the results of the algorithm in classification based on class labels. The application of this detection uses two algorithms, namely Random Forest and Logistic Regression. Both of these algorithms can predict classes with perfect accuracy in 6 experimental data, then the results of 526 experimental data resulted in a final accuracy of 0.94 for logistic regression and 0.93 for random forest. The advantage with a thin difference in this case is in the Logistic Regression algorithm.
Combination of TF-IDF and Rabin-Karp for Detecting Document Similarity in Student Thesis Abstracts Saputro, Pujo Hari; Pontoh, Fransisca Joanet; Tumurang, Olivia Maria
Jurnal Teknologi Sistem Informasi dan Sistem Komputer TGD Vol. 8 No. 1 (2025): J-SISKO TECH EDISI JANUARI
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/jsk.v8i1.10611

Abstract

Final semester students are required to complete a final project in the form of research relevant to their respective fields of study, to find innovative solutions, and to develop critical thinking skills. However, plagiarism is a common problem that often arises. Plagiarism is defined as the act of taking someone else's work, including opinions, and claiming it as one's own. Therefore, technology can be used to detect similarities in the abstracts of student manuscripts submitted during thesis title submissions, allowing for early detection of plagiarism. The corpus used was taken from the directory of final projects from the Computer Engineering Study Program, consisting of 98 data points, and from the Civil Engineering Study Program, consisting of 40 data points. In this study, utilizing the TF-IDF and Rabin-Karp algorithms, it was found that TF-IDF is capable of detecting the importance of a word in a document relative to the entire corpus. Rabin-Karp has also proven effective in detecting matching patterns in several corpuses, with a known pattern matching accuracy of 70%.
Pengembangan Sistem Digital Payment Pada UMKM Di Manado Sebagai Upaya Peningkatan Efisiensi Bisnis Saputro, Pujo Hari; Sitompul, Bernard Jumadi Dehotman; Tumurang, Olivia Maria Tumurang
Jurnal Pengabdian Masyarakat IPTEK Vol. 5 No. 1 (2025): Edisi Januari 2025
Publisher : STMIK Triguna Dharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.53513/abdi.v5i1.10612

Abstract

Usaha Mikro, Kecil, dan Menengah (UMKM) menjadi penyokong utama ekonomi tidak hanya mencerminkan kontribusi mereka dalam pertumbuhan ekonomi, tetapi juga bagaimana mereka menjadi katalisator bagi inklusi sosial dan pembangunan berkelanjutan. UMKM tidak hanya menciptakan lapangan kerja, tetapi juga berperan dalam membangun komunitas yang berkelanjutan, mendukung kemandirian lokal, dan memperkuat ikatan sosial. Berdasarkan data terakhir pusat statistik Provinsi Sulawesi Utara diketahui jumlah pelaku UMKM adalah sebanyak 53.303 usaha, sedangkan di Manado sendiri sebanyak 3.591 pelaku usaha. Untuk mendukung kemandirian dan perkembangan UMKM, perlu adanya peningkatan pengetahuan, sdm dan juga fasilitas bagi usaha yang dijalankan. Berdasarkan pengamatan yang dilakukan sebagian besar pelaku UMKM masih melakukan pembayaran yang tidak terkelola dengan baik. adapun pelaku UMKM yang sudah menerapkan sistem digital payment harus mengeluarkan biaya yang besar dikarenakan saat ini sebagian aplikasi yang ada adalah aplikasi yang berbayar. Berdasarkan uraian tersebut penulis akan mengusulkan pengembangan aplikasi digital payment serta pendampingan kepada pelaku UMKM dengan menerapkan metode intervensi CBPR untuk mencoba menanamkan kebiasan pengelolaan uang hasil penjualan pada pelaku UMKM dengan lebih baik, serta metode CBPR bertujuan untuk memenuhi kebutuhan sesuai yang didefiniskan oleh subjek.
Enhanced Advanced Multi-Objective Path Planning (EAMOPP) for UAV Navigation in Complex Dynamic 3D Environments Airlangga, Gregorius; Bata, Julius; Nugroho, Oskar Ika Adi; Sugianto, Lai Ferry; Saputro, Pujo Hari; Makin, See Jong; Alamsyah, Alamsyah
International Journal of Robotics and Control Systems Vol 5, No 2 (2025)
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/ijrcs.v5i2.1759

Abstract

Unmanned Aerial Vehicles (UAVs) have emerged as vital tools in diverse applications, including disaster response, surveillance, and logistics. However, navigating complex, obstacle-rich 3D environments with dynamic elements remains a significant challenge. This study presents an Enhanced Advanced Multi-Objective Path Planning (EAMOPP) model designed to address these challenges by improving feasibility, collision avoidance, and path smoothness while maintaining computational efficiency. The proposed enhancement introduces a hybrid sampling strategy that combines random sampling with gradient-based adjustments and a refined cost function that prioritizes obstacle avoidance and path smoothness while balancing path length and energy efficiency. The EAMOPP was evaluated in a series of experiments involving dynamic environments with high obstacle density and compared against baseline algorithms, including A*, RRT*, Artificial Potential Field (APF), Particle Swarm Optimization (PSO), and Genetic Algorithm (GA). Results demonstrate that the EAMOPP achieves a feasibility score of 0.9800, eliminates collision violations, and generates highly smooth paths with an average smoothness score of 9.3456. These improvements come with an efficient average execution time of 6.6410 seconds, outperforming both traditional and heuristic-based methods. Visual analyses further illustrate the model's ability to navigate effectively through dynamic obstacle configurations, ensuring reliable UAV operation. Future research will explore optimizations to further enhance the model's applicability in real-world UAV missions.
Sequential Modeling of News Headlines and Descriptions for Multi-Class Classification Pradana, Musthofa Galih; Saputro, Pujo Hari; Wijaya, Dhina Puspasari
International Journal of Computer and Information System (IJCIS) Vol 6, No 2 (2025): IJCIS : Vol 6 - Issue 2 - 2025
Publisher : Institut Teknologi Bisnis AAS Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29040/ijcis.v6i2.229

Abstract

Automatic classification of news content plays a vital role in organizing and filtering data for various applications such as news recommendation systems and media monitoring. This study investigates the use of Recurrent Neural Networks (RNN) and sequential modeling for multi-class classification of news data. A dataset consisting of 12,000 news sentences, categorized into four distinct classes politics, economy, sports, and technology was utilized for training and evaluation. The research focuses on comparing the performance of RNN models without optimization techniques and RNNs enhanced through optimizer implementation and sequence modeling. The baseline RNN model, trained without any optimizer or sequence enhancements, achieved a classification accuracy of 89%. By incorporating optimizer functions and leveraging sequential dependencies in both news headlines and descriptions, the proposed model demonstrated a 1% improvement, achieving an overall accuracy of 90%. These findings indicate that even a slight enhancement in modeling temporal dependencies and optimization can result in measurable gains in multi-class classification performance. The sequential combination of news headlines and descriptions is shown to be an effective strategy for capturing contextual features that improve the model’s predictive accuracy. This research contributes to the field of natural language processing by highlighting the effectiveness of sequential modeling and optimization in neural network-based text classification systems.
Classification of Tomato Ripeness Levels Using Convolutional Neural Network (CNN) Allo, Yusi Meilany Kendek; Paendong, Indah Prisilia; Saputro, Pujo Hari
Journal of Intelligent Systems and Information Technology Vol. 2 No. 2 (2025): July
Publisher : Apik Cahaya Ilmu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61971/jisit.v2i2.151

Abstract

This research develops an image-based tomato ripeness classification system using Convolutional Neural Network (CNN). The dataset consists of 3600 tomato images classified into three classes, namely unripe, half-ripe, and ripe. These images were obtained from publicly available datasets, specifically 2,550 images from Kaggle and 1,050 images from GitHub. Before testing, a pre-processing stage is carried out which includes resizing to reduce the size of the image, cropping to focus on the main object, namely tomatoes, and augmentation to increase the generalization ability of the model. The CNN model is built with an architecture that is capable of extracting visual features automatically through the learning process. The training results for 10 epochs show that the validation accuracy reaches 99.39%, with a loss of 4.16%, indicating that the model is able to learn well without significant overfitting. These results show that CNNs perform well in accurately identifying tomato maturity, while emphasizing the possibility of further improving the model through transfer learning and optimization to handle more complex data
Co-Authors A'yun, Riza Shinta Qurrota Aditiarno Manik Ahmad Sulaiman, Ahmad Airlangga, Gregorius Alamsyah - Allo, Yusi Meilany Kendek Amsy, M.R Mutawakkil Anas, Syukron Arbiati Faizah Arbiati Faizah Asep Rahman Augusta Jannatul Firdaus Batubara, Saimarrasoki Dhina Puspasari Wijaya, Dhina Puspasari Djoko Budiyanto, Djoko Dyah Listianing Tyas, Dyah Listianing Dyah ListianingTyas Fatonah, Umti Gutama, Deden Hardan Henki Bayu Seta Herlino Nanang Indana Lazulfa Izzatul Umami Julius Bata Kalambia, Audia Endondaya Kevin, Geraldy Lintang Patria Luqmanul Hakim M Fadhel Nurmidin Maddusa, Sri Seprianto Makin, See Jong Michael Aristian Musthofa Galih Pradana Nindy Irzavika Nugroho, Oskar Ika Adi Nur Arifin Nur Rachman Dzakiyullah Nurcahyo, Azriel Christian Olivia Maria Tumurang, Olivia Maria Paendong, Indah Prisilia Pamekas, Bondan Wahyu Payuk, Fajar Salinding Buntu Pontoh, Fransisca Joanet Reinhard Komansilan Rendy Syahputra Reza Augusta J.F Rizal Sengkey Rouzi, Kana Safrina Roy, Andi Rumui, Nelson Rusiyono, Ruwet Salassa, Norris Elden Sari, Susi Nopita Silvianah, Nita Siti Zubaidah Sitompul, Bernard Jumadi Dehotman Sugianto, Lai Ferry Tumurang, Olivia Maria Tumurang Viantina, Anisa Lia Victor Tarigan Wahyuni Fithratul Zalmi Wardiyana, Lisa Wardiyana, Lisa Widoyoningrum, Sri Wulandari, Narulita Zalmi, Wahyuni Fithratul