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All Journal FORUM STATISTIKA DAN KOMPUTASI dCartesian: Jurnal Matematika dan Aplikasi Jurnal TIMES JURNAL LPPM BIDANG SAINS DAN TEKNOLOGI Knowledge Engineering and Data Science Jurnal Teknik Informatika (JUTIF) Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Operations Research: International Conference Series Jurnal Ilmiah Sains Journal of Information Technology, Software Engineering and Computer Science d'Cartesian: Jurnal Matematika dan Aplikasi Jurnal Informatika dan Rekayasa Perangkat Lunak
Deiby T Salaki
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Engineering Mathematics Physics

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Comparison of Partial Least Squares Regression and Principal Component Regression for Overcoming Multicollinearity in Human Development Index Model Samosir, Ravika Dewi; Salaki, Deiby Tineke; Langi, Yohanes
Operations Research: International Conference Series Vol. 3 No. 1 (2022): Operations Research International Conference Series (ORICS), March 2022
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v3i1.126

Abstract

One of the assumptions in ordinary least squares (OLS) in estimating regression parameter is lack of multicollinearity. If the multicollinearity exists, Partial Least Square (PLS) and Principal Component Regression (PCR) can be used as alternative approaches to solve the problem. This research intends to compare those methods in modeling factors that influence the Human Development Index (HDI) of North Sumatra Province in 2019 obtained from the Central Bureau of Statistics. The result indicates that the PLS outperforms the PCR in term of  the coefficient of determination and squared error
Comparing the Performance of Prediction Model of Ridge and Elastic Net in Correlated Dataset Bastiaan, Richy Marcelino; Salaki, Deiby Tineke; Hatidja, Djoni
Operations Research: International Conference Series Vol. 3 No. 1 (2022): Operations Research International Conference Series (ORICS), March 2022
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v3i1.127

Abstract

Multicollinearity refers to a condition where high correlation between independent variables in linear regression model occurs.  In this case, using ordinary least squares (OLS) leads to unstable model. Some penalized regression approaches such as ridge and elastic-net regression can be applied to overcome the problem. Penalized regression estimates model by adding a constrain on the size of parameter regression. In this study, simulation dataset is generated, comprised of 100 observation and 95 independent variables with high correlation. This empirical study shows that elastic-net method outperforms the ridge regression and OLS.  In correlated dataset, the OLS is failed to produce a prediction model based on mean squared error (MSE)
Perbandingan Metode Triple Exponential Smoothing Additive dan Additive Parameter Damped untuk Peramalan Indeks Harga Konsumen Wawo, Rohelio Yoel; Salaki, Deiby Tineke; Komalig, Hanny Andrea Huibert; Hatidja, Djoni; Paendong, Marline Sofiana; Manurung, Tohap
Euler : Jurnal Ilmiah Matematika, Sains dan Teknologi Volume 13 Issue 1 April 2025
Publisher : Universitas Negeri Gorontalo

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37905/euler.v13i1.30928

Abstract

The purpose of this study is to compare the TES Additive and TES Additive Parameter Damped methods to forecast the Consumer Price Index (CPI) of Manado City. The data used are secondary data in the form of CPI from the Central Statistics Agency of North Sulawesi, covering the period from January 2020 to December 2023. The forecasting accuracy indicators used are Root Mean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE). The results of the analysis show that the TES Additive method has better performance in forecasting the CPI of Manado City compared to TES Additive Damped Parameter. This is because the MAPE value in the TES Additive method testing data is smaller, indicating a higher accuracy of the forecasting results. The CPI prediction for 2024 shows a stable upward trend every month. This increase is expected to be driven by economic activity that is starting to recover after the pandemic, increased public consumption, and seasonal inflation approaching religious holidays and the end of the year. The highest CPI value is predicted to reach 117.32 in December 2024.
IMPLEMENTASI METODE SUBSTITUSI OUT BY DIAGONALS PADA PENGAMANAN FILE TEKS Ramengkomole, Virgini Graditya; Ketaren, Eliasta; Salaki, Deiby Tineke
Jurnal TIMES Vol 14 No 1 (2025): Jurnal TIMES
Publisher : STMIK TIME

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Abstract

Perkembangan teknologi informasi telah memberikan kemudahan akses terhadap data, tetapi juga meningkatkan risiko keamanan data, seperti pencurian dan penyalahgunaan informasi sensitif. File teks, sebagai format dasar yang sering digunakan karena mudah dibaca dan dipahami manusia, yang rentan terhadap akses tidak sah jika dikirim tanpa perlindungan. Oleh karena itu, diperlukan metode enkripsi untuk melindungi isi file tersebut. Metode substitusi out by diagonals menjadi solusi yang menarik karena menggunakan pola matriks diagonal untuk menyandikan karakter sehingga pesan asli sulit ditebak tanpa kunci yang tepat. Data yang digunakan mencakup 76 karakter, termasuk huruf kapital, huruf kecil, angka, dan tanda baca. Hasil penelitian menunjukkan bahwa metode susbtitusi out by diagonals berhasil diimplementasikan dengan baik. Sistem mampu menghasilkan ciphertext yang aman dan dapat mengembalikan teks asli setelah proses dekripsi. Penelitian ini menyimpulkan bahwa implementasi metode substitusi out by diagonals berhasil diimplementasikan untuk pengamanan file teks dengan format .pdf dan .docx.
Implementation of an Internet of Things (IoT)-Based Water Level Early Warning System With Telegram Notification Lengkong, Agnes Gratia; Salaki, Deiby Tineke; Alfonsius, Eric
Jurnal Informatika dan Rekayasa Perangkat Lunak Vol. 6 No. 2 (2025): Volume 6 Number 2 June 2025
Publisher : Universitas Teknokrat Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33365/jatika.v6i2.349

Abstract

Water level is an important parameter in monitoring hydrological dynamics, particularly in drainage channels, as significant changes can trigger flooding and disrupt public activities, including in the Faculty of Mathematics and Natural Sciences (FMIPA) campus at Sam Ratulangi University. This study aims to design and implement an early warning system for water level based on the Internet of Things (IoT) that can provide real-time notifications via Telegram. The system utilizes the ESP32 microcontroller, HC-SR04 Ultrasonic Sensor, and Raindrop Sensor to detect rainfall and measure water levels in the drainage channel. The system is designed using the Waterfall model and tested with a black-box approach. Notifications are automatically sent to Telegram when the water level status reaches the "Alert" or "Overflow" category, and will stop once the status returns to "Safe." The test results show that the system functions accurately, the network connection is stable, and notifications are sent in a timely manner. This study contributes to local flood mitigation and enhances the preparedness of the academic community towards rising water levels.
Comparing Orientation Position in Close-Range Photogrammetry for the Documentation of Waruga Cultural Heritage as 3D Objects Salaki, Deiby Tineke; Latumakulita, Luther Aleander; Sintaro, Sanriomi; Islam, Noorul
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 2 (2025): JUTIF Volume 6, Number 2, April 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.2.4305

Abstract

Waruga is a distinctive cultural artifact found exclusively in the Minahasa region. Despite its historical and cultural significance, efforts to preserve Waruga remain inadequate. Many structures have been left neglected, covered in fungi, or even damaged over time. Additionally, government-led relocation initiatives have contributed to the loss of their original form, further threatening this invaluable Minahasa cultural heritage. This study aims to examine the impact of photographic orientation in the creation of 3D models using close-range photogrammetry techniques. The resulting 3D models will be displayed on a digital platform to support the preservation and promotion of Minahasa culture. The photography process was divided into two categories: point-of-view shots and high-angle shots. Findings indicate that the optimal angle for point-of-view shots is 15 degrees downward, while for high-angle shots, it is 30 degrees downward. Furthermore, comparative analysis of Waruga structures with varying shapes demonstrates that portrait orientation yields 3D models that more accurately resemble the original objects compared to landscape orientation when using the same number of images. The study concludes that portrait orientation is the most effective approach for 3D reconstruction of Waruga, offering advantages such as faster processing times and reduced file sizes. In contrast, landscape orientation presents challenges, including difficulties in capturing intricate details, increased processing time, and larger file sizes. These findings provide valuable insights into optimizing digital preservation techniques for Waruga and other cultural heritage artifacts.
Human Facial Expressions Identification using ConvolutionalNeural Network with VGG16 Architecture Latumakulita, Luther Alexander; Lumintang, Sandy Laurentius; Salaki, Deiby Tineke; Sentinuwo, Steven R.; Sambul, Alwin Melkie; Islam, Noorul
Knowledge Engineering and Data Science
Publisher : citeus

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Abstract

The human facial expression identification system is essential in developing human interaction and technology. The development of Artificial Intelligence for monitoring human emotions can be helpful in the workplace. Commonly, there are six basic human expressions, namely anger, disgust, fear, happiness, sadness, and surprise, that the system can identify. This study aims to create a facial expression identification system based on basic human expressions using the Convolutional Neural Network (CNN) with a 16-layer VGG architecture. Two thousand one hundred thirty-seven facial expression images were selected from the FER2013, JAFFE, and MUG datasets. By implementing image augmentation and setting up the network parameters to Epoch of 100, the learning rate of 0,0001, and applying in the 5Fold Cross Validation, this system shows performance with an average accuracy of 84%. Results show that the model is suitable for identifying the basic facial expressions of humans
Co-Authors Aditya Lapu Kalua Alfonsius, Eric Altien J Rindengan Anang Kurnia Angelina Monica Gulla Arief Gusnanto Bagus Sartono Bastiaan, Richy Marcelino Djoni Hatidja Eliasta Ketaren, Eliasta Faranika Deysi G Maneking Ferelda Pasriyane Kele Gloria Rompas Hamel, Jeliana Hanny A. H. Komalig Hanny Andrea Huibert Komalig Hanny Komalig I Wayan Mangku Islam, Noorul Jantje D. Prang Jantje D. Prang John S. Kekenusa Komalig, Hanny Andrea Huibert Latumakulita, Luther Aleander Lengkong, Agnes Gratia Lumintang, Sandy Laurentius Luther Latumakulita Maingga, Dikky Malvino Mait, Yeremia Armstrong Majore, Marzena Maneking, Faranika Deysi G Marline Paendong Marzena Majore Ramengkomole, Virgini Graditya Sambul, Alwin Melkie Samosir, Ravika Dewi Sanriomi Sintaro Steven Ray Sentinuwo Tohap Manurung Tohap Manurung Veronika H Wawo, Rohelio Yoel Yeremia Armstrong Mait Yohanes Langi