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All Journal Lontar Komputer: Jurnal Ilmiah Teknologi Informasi Jurnal Teknologi Informasi dan Ilmu Komputer Indonesia Symposium on Computing Indonesian Journal on Computing (Indo-JC) IJoICT (International Journal on Information and Communication Technology) Jurnal Teknologi dan Sistem Komputer JURNAL MEDIA INFORMATIKA BUDIDARMA Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control INTEK: Jurnal Penelitian Building of Informatics, Technology and Science Journal of Computer System and Informatics (JoSYC) Charity : Jurnal Pengabdian Masyarakat Mosharafa: Jurnal Pendidikan Matematika Prosiding Konferensi Nasional PKM-CSR eProceedings of Engineering Jurnal Abdimas Jurnal Komtika (Komputasi dan Informatika) LOGIC: Jurnal Penelitian Informatika
Irma Palupi
Universitas Telkom
Arts Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Mathematics Nursing Public Health Social Sciences Other

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Prediksi Perubahan Kondisi Uptrend Dan Downtrend Pada Pasar Saham Dengan Menggunakan Model Artificial Neural Network Ann Brigita Tenggehi; Irma Palupi; Erwin Budi Setiawan
eProceedings of Engineering Vol 9, No 3 (2022): Juni 2022
Publisher : eProceedings of Engineering

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

Abstract

Abstrak Prediksi adalah sebuah proses untuk memperkirakan sesuatu secara sistematis dan memperkecil adanya kesalahan dimana hasilnya merupakan yang paling mungkin terjadi di masa depan berdasarkan informasi masa lalu dan masa sekarang. Masalah yang diangkat pada penelitian ini adalah Perubahan Kondisi harga naik (uptrend) dan harga turun (downtrend) pada Pasar Saham. Penelitian bertujuan untuk memprediksi Kondisi harga naik (uptrend) dan harga turun (downtrend) Pasar Saham Indonesia yang dipengaruhi oleh Pasar Saham Global. Prediksi dilakukan dengan menggunakan data saham dari 8 Data Harga Saham dari beberapa negara di dunia yaitu Indonesia (^JKSE), Kuala Lumpur (^KLSE),Singapura (^STI),China (000001.SS),Hong Kong (^HSI),Korea (^KS11),Jepang(^N225),dan United States(^DJI). Model yang digunakan adalah Artificial Neural Network (ANN) dimana model ini akan memprediksi harga naik dan harga turun berdasarkan data close dari Pasar Saham Indonesia (^JKSE). Hasil pengujian dari model yang dibangun memberikan nilai Train Accuracy tertinggi yaitu model ANN-05 dengan hasil yang ditampilkan adalah 76.74%. Model dengan nilai Test Accuracy tertinggi yaitu model dari ANN-01 dengan hasil 71.55% dengan menggunakan Node Hidden Layer 16,32,64 dan 128. Kata Kunci : Prediksi,Pasar Saham,Artificial Neural Network (ANN).
Computational Parallel on Simulation of Wave Attenuation by Mangrove Forest Putu Harry Gunawan; Irma Palupi; Nurul Ikhsan; Iryanto Iryanto; Naila Al Mahmuda
Lontar Komputer : Jurnal Ilmiah Teknologi Informasi Vol 14 No 3 (2023): Vol. 14, No. 3 December 2023
Publisher : Institute for Research and Community Services, Udayana University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24843/LKJITI.2023.v14.i03.p02

Abstract

Coastal ecosystems, specifically mangrove trees, safeguard coastal regions against natural disasters like erosion, floods, and tsunamis. Numerical simulations employing the Shallow Water Equation (SWE), encompassing mass and momentum conservation equations, are used to comprehend how mangroves attenuate wave energy. The SWE incorporates Manning's friction term, which is directly influenced by mangrove forests. However, the SWE's complexity and sensitivity to initial conditions hinder analytical solutions. Despite its increasing computational demands, we utilize the robust staggered grid method to address this challenge. Our study examines mangroves' wave-attenuating effects and introduces a parallel computational model using OpenMP to expedite computations. Findings reveal that mangroves can reduce wave amplitudes by up to 33% when employing a Manning's coefficient of 0.3 within confined basin simulations. Furthermore, our parallel computing experiments demonstrate substantial computation speed enhancements; the speedup improves up to a point, with a notable 7.26-fold acceleration observed when utilizing eight threads compared to a single line. Moreover, more than a 10-fold acceleration is observed when the number of threads is greater than 16. This underscores the significance of parallelization in exploring mangrove contributions to coastal protection.
Predicting Forest Fire Hotspots with Carbon Emission Insights Using Random Forest and Gradient Boosting Regression irma palupi; bambang ari wahyudi; Naila AL Mamuda; Ayu Shabrina
International Journal on Information and Communication Technology (IJoICT) Vol. 9 No. 2 (2023): Vol.9 No. 2 Dec 2023
Publisher : School of Computing, Telkom University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.21108/ijoict.v9i2.865

Abstract

This research paper focuses on predicting the dispersion of carbon emissions, a crucial indicator for identifying potential forest fire hotspots in the wooded regions of Sumatra Island, Indonesia. Forest fires, often triggered by extended periods of dry weather, result in significant environmental degradation, impacting both the ecosystem and the economy. Furthermore, health concerns arise from smoke inhalation, leading to respiratory problems. To achieve this predictive capability, we harnessed valuable datasets, including GFED4.1s for carbon emissions and ERA5 for historical climate indicators, spanning from 1998 to 2022. Employing supervised learning ensemble methods, specifically Random Forest Regression (RFR) and Gradient Boosting Regression (GBR), we sought to forecast carbon emissions. It is noteworthy that our predictions encompassed carbon emission values from 1998 to 2023, providing insights into recent trends. Our analysis showed that GBR did better than RFR in terms of evaluation metrics, with a root mean square error (RMSE) of 10.87 and a mean absolute error (MAE) of 2.91. This was done by carefully tuning the hyperparameters. Additionally, our study highlighted that precipitation, temperature, and humidity were the primary climate factors influencing carbon emission values.
Comparative Assessment of Low Job Competitiveness Among University Graduates Using Naïve Bayes and KNN Algorithms Hamonangan, Ricardo; Palupi, Irma; Gunawan, Putu Harry
Building of Informatics, Technology and Science (BITS) Vol 6 No 1 (2024): June 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i1.5406

Abstract

Tracer studies investigate the career outcomes of graduates, encompassing job search experiences, employment conditions, and the application of acquired skills post-graduation. These studies are pivotal for universities and colleges to assess graduate success and shape educational policies. This study aims to elucidate the factors contributing to low job competitiveness through the application of classification models like KNN and Naïve Bayes. It also evaluates how competencies developed during university studies impact this scenario. Key issues addressed include the identification of factors causing low job competitiveness and the assessment of competencies trained during university education. Utilizing a dataset comprising two classes and seven features, the KNN method achieved an accuracy of 71.00%, while Naïve Bayes achieved 70.00%. The data set size is 1853 (around 20% of the survey sample) of unemployed alumni. The results indicate that the lack of specific competencies, particularly those related to practical skills and real-world application, is a major factor contributing to low job competitiveness. The results highlight a specific competency as most crucial in the KNN model, whereas different competencies play significant roles in the Naïve Bayes model. Despite variations in competency importance across models, all features significantly contribute to predictions. This research enhances the classification of workforce competitiveness levels within tracer studies and underscores the potential of KNN and Naïve Bayes algorithms to identify factors influencing low job competitiveness. These findings support informed decision-making in academic and career development initiatives, emphasizing the critical influence of university-trained competencies on job market readiness.
Decision Tree Algorithm for Predicting Alumni Job Competitiveness Through Waiting Time Working Panuluh, Bagus; Palupi, Irma; Gunawan, Putu Harry
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5647

Abstract

The absorption of alumni from universities into the world of work is an essential indicator that universities must pay attention to. One-way universities can pay attention to their alums is through tracer studies, where they can evaluate their curriculum's relevance to what is needed in today's world of work. One aspect that can be seen from the tracer study to assess the competitiveness of alums is the waiting time for alums to get their first job. This is because the sooner alums get jobs, the better the curriculum the university provides to students. This research aims to apply machine learning to predict the waiting time for alums from Telkom University to get their first job and find out what factors influence the waiting time for work. The algorithm used in the research is the Decision Tree with hyperparameter tuning using Grid Search and feature selection application. There are 3 methods of feature selection used for comparison: Spearman's Rank Correlation, Chi-square, and Principal Component Analysis. This research produces the best prediction model in applying Chi-square and hyperparameter tuning with an accuracy of 0.79, recall of 0.79, precision of 0.80, and F1-Score 0.75. Several features, such as the number of companies registered, how to find and get work, internship and practicum experience, ethical competency, discussion, and IT skills, have the biggest effects on the model.
Predicting University Graduates Employability Using Support Vector Machine Classification Haikal, Muhamad Fachri; Palupi, Irma
Building of Informatics, Technology and Science (BITS) Vol 6 No 2 (2024): September 2024
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v6i2.5655

Abstract

The absorption of graduates into the world of work is a key indicator of higher education institution success, especially amid the tight job market competition due to increasing graduate numbers. Understanding employability and the factors that influence it is crucial for higher education institution to enhance education quality and facilitate graduates' transitions to employment. This research aimed to predict the employability of Telkom University students through their initial job income. Methods involved feature manipulation techniques like Principal Component Analysis, Spearman's rank correlation, and the Chi-square test of independence, followed by SMOTE-ENN to address data imbalance. Modeling was conducted using a Support Vector Machine with Randomized Search hyperparameter tuning, analyzed through Permutation Feature Importance to identify factors affecting employability. The result showed the enhanced SVM model with SMOTE-ENN, Spearman’s rank correlation coefficient as feature selection and randomized search hyperparameter tuning achieved the highest precision, recall, f-score, and accuracy of approximately 0.70, 0.73, 0.71, and 0.73, respectively. Competency features such as ethics, english skills, IT skills, and knowledge were identified as the most influential factors.
PENGOLAHAN DATA AKUNTANSI BERBASIS PHYTON (PELATIHAN BAGI SISWA SMKN 3 BANDUNG) Puspandari, Diyas; Palupi, Irma; Fitriyani, Fitriyani
Prosiding Konferensi Nasional Pengabdian Kepada Masyarakat dan Corporate Social Responsibility (PKM-CSR) Vol 6 (2023): INOVASI PERGURUAN TINGGI & PERAN DUNIA INDUSTRI DALAM PENGUATAN EKOSISTEM DIGITAL & EK
Publisher : Asosiasi Sinergi Pengabdi dan Pemberdaya Indonesia (ASPPI)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37695/pkmcsr.v6i0.1893

Abstract

Program pelatihan ini dirancang untuk siswa sekolah kejuruan agar terampil menganalisis dan mendapatkan pengalaman praktis dalam menganalisis data. Pelatihan ini mencakup pengenalan konsep dasar pemrograman dan pengolahan data menggunakan Python. Setelah menyelesaikan program ini, diharapkan siswa memiliki dasar yang kuat untuk menunjang karir dalam bidang analisis data akuntansi. Metode yang dipakai dalam kegiatan ini adalah pelatihan, siswa langsung praktik mengolah data dipandu oleh pembicara dan instruktur. Siswa belajar cara menggunakan perangkat lunak analisis data, yaitu Python dan Ms. Excel. Pelatihan ini memberikan pengalaman praktis, karena siswa dapat menerapkan keterampilan mereka pada masalah akuntansi. Evaluasi dilakukan untuk mengetahui pemahaman peserta terhadap materi yang telah diberikan. Rata-rata perolehan nilai siswa untuk materi pemograman dasar dengan Python adalah 62,06 dengan distribusi relatif normal. Sedangkan untuk penguasaan materi pengolahan data akuntansi, nilai rata-ratanya siswa adalah 82. Hasil evaluasi menunjukkan bahwa peserta cenderung lebih mudah memahami materi pengolahan data dibandingkan materi pemograman dengan bahasa Python.
Enhancing Accuracy on Chronic-Kidney Disease Detection Using Machine Learning with Technique of Resampling and Missing Value Treatment Wibowo, Muhammad Raihan; Palupi, Irma
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control Vol. 8, No. 4, November 2023
Publisher : Universitas Muhammadiyah Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22219/kinetik.v8i4`.1761

Abstract

Chronic kidney disease is one of the deadliest diseases in the world. It is important to identify chronic kidney disease at an early stage, so that treatment and prevention can be carried out early. This study used linear interpolation method to treat the missing values, resampling using SMOTE method, and several feature selection methods, such as Pearson’s correlation coefficient and Principal component analysis. For the classification methods, Support Vector Machine and Logistic Regression were used to build prediction models for chronic kidney disease based on dataset on UCI Machine Learning. To measure the performance of the model, several test scenarios were tested out so it can be compared to the previous research on the detection of chronic kidney disease, which is used as a benchmark for this study. The best result from the experiment is obtained from the scenario of resampling using SMOTE and feature selection using Principal Component Analysis with averaged accuracy, precision, and f1-score respectively are 98,8%, 100%, dan 98,77%.
Algoritma Reversible Data Hiding dalam Mengamankan Karya Seni Gambar Digital Sadewa, I Made Aditya Putra; Wahyudi, Bambang Ari; Palupi, Irma
INTEK: Jurnal Penelitian Vol 12 No 1 (2025): April 2025
Publisher : Politeknik Negeri Ujung Pandang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31963/intek.v12i1.5193

Abstract

In today's digital era, protecting digital artworks, particularly images, has become increasingly important to prevent copyright infringement and forgery. This paper proposes a novel method for embedding secret data into images using Reversible Data Hiding (RDH) techniques that leverage histogram shifting and random sub-blocks. The method is designed to maintain the visual integrity of the image while allowing the insertion of critical information, such as copyright metadata. The dataset used consists of 13 digital artworks sized 1280x720 pixels in PNG format, reflecting a diversity of textures and colors. Experimental results demonstrate that the proposed method achieves a high embedding capacity with PSNR values exceeding 37 dB, indicating excellent image quality post data insertion. Additionally, the method exhibits resilience against illegal modifications, with the ability to detect changes in images that have had data embedded. By integrating a PIN-based authentication system, the method enhances the security and integrity of the embedded information. This research significantly contributes to the field of digital artwork protection, offering an effective solution to preserve the authenticity and aesthetic value of images while enabling secure and reversible data insertion. The findings underscore the potential of RDH techniques in safeguarding sensitive information across various applications, ensuring that digital artworks can be both protected and enjoyed without compromising their quality.
Mental Health Sentiment Analysis on Twitter using Ensemble Learning Algorithm Aziz, Kemal; Wahyudi, Bambang Ari; Palupi, Irma
Building of Informatics, Technology and Science (BITS) Vol 7 No 2 (2025): September 2025
Publisher : Forum Kerjasama Pendidikan Tinggi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bits.v7i2.7763

Abstract

Mental health problems have become an important health issue around the world. Poor understanding as well as low mental health awareness contribute to mental health healing efforts. In particular, Social media is becoming a platform for people to convey feelings and emotions. A dataset of 20,000 English tweets, equally divided into 10,000 depressed and 10,000 non-depressed tweets, which were cleaned and processed using Term Frequency-Inverse Document Frequency (TF-IDF) for feature extraction. The method used in this sentiment analysis introduces an ensemble learning framework that combines Naïve Bayes, Support Vector Machine, and Random Forest classifiers, using majority voting for prediction. Each classifier was optimized using the best parameters, and the models were validated through 5-fold cross-validation. The experimental results show that Naïve Bayes with α = 1 achieved an accuracy of 76.23% while Random Forest with 5000 trees at 76.77%, and Support Vector Machine with a linear kernel at 75.32%. By combining these classifiers, the ensemble model reached the highest accuracy of 77.88%, demonstrating the effectiveness of combining multiple models to improve performance.
Co-Authors Aditya Ramadhan Adiwijaya Adriani, Zahrina Aulia AL Mamuda, Naila Andri Saputra Anton Sri Haryanto Apriani, Iklima Ardhia Pringgowati Ayu Shabrina Ayu Shabrina Aziz, Kemal Bambang Ari Wahyudi Brigita Tenggehi Deni Saepudin Diah Fitri Wulandari Diani Sarah Kamilial Diani Sarah Kamilial Diyas Puspandari Erwin Budi Setiawan Fauzi Dzulfiqar Wibowo Firdaus Maringga Firdaus Maringga Firdaus Maringga Fitriyani Fitriyani Haikal, Muhamad Fachri Hamonangan, Ricardo Harry Susilodharma Husain Athfal Hidayat I Wayan Ade Sugisnawan Dandysmara I Wayan Ade Sugisnawan Dandysmara Indra Utama Sitorus Indwiarti Iryanto Iryanto Jondri Jondri Khairi, Muhammad Daffa Mahmud Imrona Megi Rahma Dony Muh. Arfan Arsyad Muhammad Faizal Muhammad Saddam Salsabillah Muhammad Saddam Salsabillah Muhammad Saddam Salsabillah Naila Al Mahmuda Naila AL Mamuda Ni Putu Winda Ardiyanti Nur Roza Fitriyana Nurul Ikhsan Panuluh, Bagus Prasetyo, Huda Rizky Priscilia Lovita Paelongan Putranto, Muhammad Fadhlan Putu Harry Gunawan Rian Febrian Umbara Rinaldi Wibiyanto Riyadi Lazuardi Sirait Rohidi Rohidi Sadewa, I Made Aditya Putra Satrio, M Siti Saadah Suleman, Annisa Rahmawati varid vaya yusuf Wibowo, Muhammad Raihan Yuliant Sibaroni