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All Journal Voteteknika (Vocational Teknik Elektronika dan Informatika) JOIV : International Journal on Informatics Visualization International Journal of Artificial Intelligence Research JIPI (Jurnal Ilmiah Penelitian dan Pembelajaran Informatika) Building of Informatics, Technology and Science Jurnal Mantik Jutisi: Jurnal Ilmiah Teknik Informatika dan Sistem Informasi Progresif: Jurnal Ilmiah Komputer Jurnal JTIK (Jurnal Teknologi Informasi dan Komunikasi) Jurnal Informatika dan Sistem Informasi JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Jurnal Pendidikan dan Teknologi Indonesia Journal of Information Technology (JIfoTech) Jurnal Abdimas Bina Bangsa COMSERVA: Jurnal Penelitian dan Pengabdian Masyarakat Jurnal Informatika Teknologi dan Sains (Jinteks) Malcom: Indonesian Journal of Machine Learning and Computer Science DEVOTE: Jurnal Pengabdian Masyarakat Global Jurnal INFOTEL SmartComp SAINTEK Infact: Jurnal Sains dan Komputer Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) UPGRADE: Jurnal Pendidikan Teknologi Informasi
Haeni Budiati
Universitas Kristen Immanuel
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Social Sciences Other

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Analisis Sentimen Masyarakat Terhadap Direktorat Jenderal Pajak Juli Supriyanto Gea; Haeni Budiati; Kristian Juri Damai Lase; Sunneng Sandino Berutu
Infact: International Journal of Computers Vol. 8 No. 01 (2024): Jurnal Sains dan Komputer
Publisher : Universitas Kristen Immanuel

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61179/jurnalinfact.v8i01.466

Abstract

One process of recognizing opinions in the form of text to determine emotions neutrally, positively, or negatively is called sentiment analysis. Sentiment analysis will explore people's emotions from texts conveyed through various social media. Directorate general of taxes as data taken from twitter for analysis was used by the author to support research. Data collection is done by searching and collecting data with tax keywords from twitter, so that it can be analyzed to determine public sentiment. From the data collected and analyzed, 40.5% were neutral, 39.4% positive, and 20.1% negative.
Implementasi Library Textblob dan Metode Support Vector Machine Pada Analisis Sentimen Pelanggan Terhadap Jasa Transportasi Online Laia, Yardiana; Berutu, Sunneng Sandino; Sumihar, Yo’el Pieter; Budiati, Haeni
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.v6i1.5090

Abstract

Online transportation services have become an inseparable part of human life today. This research aims to develop an effective sentiment analysis method to measure public opinion about the quality of online transportation services, which has a significant impact on company reputation and public acceptance of these services. In this research, we propose the use of TextBlob library to perform sentiment analysis of public opinion on online transportation services. This library allows to measure the positive, negative and neutral polarity and subjectivity of opinion text collected from Gojek, Maxim and Grab application reviews through Google Play Store. Sentiment analysis steps are carried out starting from data preparation, data pre-processing, data labeling using the Text Blob library. Furthermore, building a sentiment classification model based on the Support Vector Machine (SVM) algorithm through training and testing stages. Model testing results are evaluated with confusion matrix. The results of the analysis with textblob showed that online transportation received the highest positive sentiment of 40.1%, followed by neutral sentiment of 26.7% and negative sentiment of 25.2%. Meanwhile, the model performance measurement results show that the precision obtained the highest value in positive sentiment of 0.93. The recall parameter reaches the highest value in negative sentiment of 0.95 and f1-score in neutral and positive sentiment of 0.92. Thus, this research not only contributes to the development of sentiment analysis classification, but also has a significant practical impact in improving online transportation services and providing useful information to the public, thus encouraging innovation and continuous improvement in online transportation services.
Analisis Komparasi Performa Metode Deteksi Tepi Sebagai Predektor Diabetes Berbasis Citra Lidah: Comparative Analysis of the Performance of Edge Detection Methods as a Diabetes Predictor Based on Tongue Imagery Olam, Enos Nikodemus; Himamunanto, Agustinus Rudatyo; Budiati, Haeni
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1393

Abstract

Jenis Penyakit kencing manis atau juga biasa disebut Diabetes merupakan gangguan metabolik yang disebabkan oleh tingginya kadar gula dalam darah. Hormon insulin memindahkan gula darah ke seluruh sel tubuh, untuk disimpan atau digunakan sebagai energi. Ketika Anda menderita diabetes, tubuh sulit untuk memproduksi insulin untuk memenuhi kebutuhan tubuh dan tubuh kurang efisien dalam mengelola insulin dengan baik sesuai dengan kebutuhannya. Dalam hal ini, diabetes melitus tercatat sebagai penyebab kematian terbesar di dunia. Tanda-tanda dan efek samping penyakit diabetes melitus seharusnya terlihat secara lahiriah melalui bagian-bagian tubuh manusia, misalnya saja lidah yang menunjukkan adanya pertumbuhan atau Candida Albicans, dimana lidah adalah partikel tubuh manusia yang cukup peka terhadap rangsangan .Teknik Informatika berperan dalam penelitian ini, dengan menggunakan You Only Live Once (YOLO) sebagai media penandaan bagian tertentu dari suatu objek yang nantinya akan digunakan untuk mendeteksi tepi objek yang ditandai dan diproses dalam hal ini citra lidah untuk prosedur deteksi tepi. Untuk analisis perbandingan deteksi tepi citra lidah dalam deteksi penyakit diabetes melitus, sistem dapat menghasilkan hasil keluaran yang cukup memuaskan.
Analisis Performa Metode HTB - Traffic Shaping Layer 7 Pada Manajemen Bandwidth Jatmika; Putri, Farelia Devi Julia; Sumihar, Yo’el Pieter; Budiati, Haeni
Jurnal Informatika dan Sistem Informasi Vol. 9 No. 2 (2023): Jurnal Informatika dan Sistem Informasi
Publisher : Universitas Ciputra Surabaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37715/juisi.v9i2.4214

Abstract

Internet sangat dibutuhkan untuk mendapatkan informasi. Internet merupakan komponen penting dalam bidang pendidikan, perkantoran, bisnis, perusahaan, dan lain sebagainya. Perkembangan Internet semakin pesat, tetapi jika tidak dimanajemen dengan baik bandwidth-nya akan mempengaruhi kinerja jaringan. Pada jaringan Internet saat ini sering terjadi masalah, dimana pada layanan tertentu bisa mengkonsumsi bandwidth dalam jumlah besar mengakibatkan layanan lain tidak bisa mendapat bandwidth sesuai kebutuhan. Penelitian ini melakukan pembagian bandwidth ke dalam beberapa kelas, melakukan pembatasan traffic pada tiap level, membuat alokasi aktivitas upload dan download. Manajemen bandwidth dilakukan dengan menggunakan metode HTB (Hierarchical Token Bucket). Pembagian bandwidth dilakukan secara hirarki yang dibagi ke dalam beberapa kelas. Performa jaringan lebih terjamin jika menambahkan monitoring traffic dengan menggunakan metode Traffic Shaping. Berdasarkan hasil penelitian, didapatkan performa jaringan menggunakan standart TIPHON untuk pengukuran parameter performa jaringan menggunakan metode HTB (Hierarchical Token Bucket) dan Traffic Shaping memiliki rata-rata 3,5 dan termasuk dalam kategori “Baik”, sedangkan pengujian tanpa menggunakan metode HTB (Hierarchical Token Bucket) dan Traffic Shaping memiliki rata-rata 2,75 dan termasuk kategori “Kurang Baik”.
Pengenalan Emosi pada Citra wajah menggunakan Metode YOLO Gallu, Apliana; Himamunanto, AR.; Budiati, Haeni
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.444

Abstract

Human emotions can be expressed through facial expressions, and automatic recognition has a wide range of applications, from human and computer interaction to behavior analysis. Researchers developed a YOLO-based model that was trained to recognize various basic emotions such as happy, sad, angry, and surprised. The dataset used includes various facial images with corresponding emotion labels. This research produced a web to detect human faces using the YOLO algorithm in realtime. A total of 400 photos were used in the analysis; these images were separated into 4 classes: happy, sad, angry, and surprised. Of the 400 images, 70% are training images, 20% are validation images, and 10% are test images. There were 200 epochs of training data, which resulted in a new model. The validation rate of the mAP is 90%, the final score of the model shows that the object identification accuracy of the YOLOv8 model on facial expressions is at the highest point. The experimental results show that the YOLO method is able to detect and classify emotions with a high degree of accuracy. These results demonstrate its advantages in speed and efficiency compared to other more conventional methods. This implementation opens up opportunities for further development in real-time applications that allow the YOLO method to be used in a variety of applications.
Analisis Performa Raytracing dan MCMC Pada Realisme Visualisasi Obyek 3D Dengan Terintegrasi MIPMapping Budet, Vincensa Woytimena; Himamunanto, Agustinus Rudatyo; Budiati, Haeni
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.436

Abstract

The development of computer graphics has resulted in an increasingly realistic and immersive digital world, especially in the field of 3D object representation. One of the techniques for image presentation is ray tracing, however, regular ray tracing requires long computation time. To achieve high realism in 3D objects, complex computational operations and the use of appropriate algorithms are required. In this research, Markov chain Monte carlo (MCMC) algorithm has the potential to achieve realism on a 3D object. This research analyzes the performance comparison between ordinary ray tracing and MCMC algorithm in achieving realism on 3D objects and integrating Mipmapping technology to improve the visual quality of 3D objects. The results are measured by calculating the PSNR value on the rendered object and comparing the noise level of a 3D object rendered with ordinary ray tracing, and ray tracing using the Monte carlo algorithm. The number of samples used were 50 samples of 3D objects tested with Monte Carlo and obtained a result of 94%, and with ordinary ray tracing of 6% which is indicated by the level of distortion or error that occurs in the processed object. This shows that by rendering using the MCMC algorithm the image quality of the rendered object is better than rendering using ordinary ray tracing
Analisis Performa Metode Perceptual Color Transfer Dalam Peningkatan Kualitas Citra Ina, Osmanila Tamo; Himamunanto, AR; Budiati, Haeni
Kesatria : Jurnal Penerapan Sistem Informasi (Komputer dan Manajemen) Vol 5, No 3 (2024): Edisi Juli
Publisher : LPPM STIKOM Tunas Bangsa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/kesatria.v5i3.420

Abstract

The eye, as the sense of human vision, not only serves to see objects but also builds perceptions of the objects seen so that, in this case, it can judge images from different perspectives. Improved image quality is required because images often experience decreased quality caused by many factors, including being too dark, blurred, less sharp, too bright, and other factors. Perceptual Color Transfer is one of the most popular methods used in research. This method changes the color of an image to match the characteristics of another image, while maintaining the visual quality and naturality of the image. By considering the way humans perceive color, this method produces visual and consistent color adjustments that can contribute to improving the overall image quality. The color spaces used in this study are the lαβ and HSV color spaces using the MSE (Mean Square Error) and PSNR (Peak Signal to Noise Ratio) parameters. The results of the study show that the Perceptual Color Transfer method can be a good alternative to image processing techniques in light and dim light conditions, with the best average MSE and PSNR results in dark source image color transfer in the HSV color space of 0.0678021 and 21.43221, as well as the best mean results in light source image color transfer in Lαβ spaces of 0.0608865 and 20.03709.
Identifikasi Pola Obyek Kain Tenun Sumba dengan Menggunakan Metode K-Nearest Neighbor (KNN) Budiati, Haeni; Himamunanto, Agustinus Rudatyo; Bolo, Naomi Tena
Upgrade : Jurnal Pendidikan Teknologi Informasi Vol 1 No 1 (2023)
Publisher : Pendidikan Teknologi Informasi Universitas Bumigora

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30812/upgrade.v1i1.3149

Abstract

Woven fabrics originating from Sumba have their own patterns that distinguish them from other woven fabric patterns throughout Indonesia. The pattern is a distinctive feature that describes the culture of the people in Sumba which is very diverse. To distinguish fabric patterns, one of the algorithms for object recognition is the K-Nearest Neighbor (KNN) algorithm. The KNN algorithm classifies objects based on training data that is closest to the object. Processing works by using metric and eccentricity parameters on training data and input images. This processing will produce text data which is the identification of objects in Sumba woven fabric motifs. Based on the testing that has been done, it successfully identifies the type of object contained in the training data. For types of objects that are not contained in the training data, identification is based on their proximity to the types of objects in the group that contain Sumba woven fabric patterns. The accuracy level of Sumba woven fabric pattern object identification in testing 70 different fabric motif images obtained 62 objects in the input image can be identified correctly (88.57%), while 8 objects in the input image cannot be identified (11.43%).
Analisis Performa Metode Yolo Untuk Deteksi Hyperlipidemia Berdasarkan Klasifikasi Citra Corneal Arcus Supriadi, Joseph; Himamunanto, Agustinus Rudatyo; Budiati, Haeni
Progresif: Jurnal Ilmiah Komputer Vol 20, No 2: Agustus 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i2.2088

Abstract

Hyperlipidemia is a medical condition with high blood lipid levels that increase the risk of cardiovascular disease. A physical indicator of hyperlipidemia is Corneal Arcus, a white ring around the cornea. This study analyzes the ability of the YOLO (You Only Look Once) method to detect and classify Corneal Arcus in eye images. The dataset consists of 348 eye images in three categories: normal, at-risk, and Corneal Arcus. Results show the YOLO model achieved 88.9% accuracy in detecting Corneal Arcus, with precision, recall, F1-score, and mean average precision (MAP) of 88.9%, 89.2%, 88.8%, and 88.9%, respectively. These findings indicate significant potential for the YOLO method in technical applications within informatics. Although not yet validated for medical use, this research aims to share basic scientific ideas.Keywords: YOLO; Hyperlipidemia; Corneal Arcus; Image Classification AbstrakHyperlipidemia adalah kondisi medis dengan kadar lipid darah tinggi yang meningkatkan risiko penyakit Kardiovaskular. Indikator fisik hyperlipidemia adalah Corneal Arcus, cincin putih di sekitar kornea. Penelitian ini menganalisis kemampuan metode YOLO (You Only Look Once) dalam mendeteksi dan mengklasifikasikan Corneal Arcus pada citra mata. Dataset terdiri dari 348 gambar mata dalam tiga kategori: normal, berisiko, dan Corneal Arcus. Hasil menunjukkan model YOLO mencapai akurasi 88,9% dalam mendeteksi Corneal Arcus, dengan presisi, recall, F1-score, dan mean average precision (MAP) masing-masing sebesar 88,9%, 89,2%, 88,8%, dan 88,9%. Temuan ini menunjukkan potensi besar metode YOLO dalam aplikasi teknis di bidang informatika. Meskipun belum tervalidasi untuk penggunaan medis, hasil ini bertujuan untuk membagikan ide ilmiah dasar.Kata kunci: YOLO; Hyperlipidemia; Corneal Arcus; Klasifikasi Citra;
Komparasi Metode K-NN Dan K-Means Untuk Klasifikasi Buah Mangga Apriani, Apriani; Himamunanto, A. R.; Budiati, Haeni
Progresif: Jurnal Ilmiah Komputer Vol 20, No 2: Agustus 2024
Publisher : STMIK Banjarbaru

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35889/progresif.v20i2.2152

Abstract

This Research aim of comparing the modified K-means method for classification processing of training models (Supervised) with the K-NN method using the Mango Arumanis, Golek, Madu and Gedong classes. Feature extraction used in processing classification methods is based on shape characteristics consisting of metric and eccentricity. The research results obtained that the percentage precision of the K-NN method was Arumanis: 0.9%, Madu: 0.9%, Gedong: 0.9%, Golek: 0.8%, and the K-Means method was Arumanis: 0.7%, Honey: 0.9%, Gedong: 0.9%, Golek 0.6%. The recall percentage of the K-NN method is Arumanis: 0.90%, Madu: 0.75%, Gedong: 1.00%, Golek: 0.89% and the K-means method is Arumanis: 0.70%, Madu: 0 .64%, Gedong: 1.00%, Golek: 0.86%. The accuracy percentage of the K-NN classification method is Arumanis: 94.59%, Madu: 89.74%, Gedong: 97.22%, Golek: 92.11% and the K-Means method is Arumanis: 83.78%, Madu: 83.78%, Gedong: 96.88%, Golek: 86.11%. For global precision, recall and accuracy values, the K-NN method is greater than the K-Means method. Thus, the K-Means classification method which was modified to use supervised training data is still not as good as the K-NN method in classifying mango fruit types. It is hoped that the accuracy of the method for classifying mango fruit plant types by extracting shape characteristics can obtain uniform shape quality.Keywords: Image Processing; Feature Extraction; K-means, K-NN; Metric, EccentricityAbstrakPenelitian dengan tujuan komparasi Metode K-means yang dimodifikasi untuk pemrosesan klasifikasi model pelatihan (Supervised) dengan MetodeK-NN mempergunakan kelas Mangga Arumanis, Golek, Madu dan Gedong. Ekstraksi ciri yang dipergunakan dalam pemrosesan metode klasifikasi berdasarkan ciri bentuk yang terdiri dari metric dan eccentricity. Hasil penelitian memperoleh presentase precision metode K-NN adalah Arumanis: 0,9%, Madu: 0,9%, Gedong: 0,9%, Golek: 0,8%, dan metode K-Means adalah Arumanis: 0,7%, Madu: 0,9%, Gedong: 0,9%, Golek 0,6%. Presentase recall metode K-NN adalah Arumanis: 0,90%, Madu: 0,75%, Gedong: 1,00%, Golek: 0,89% dan metode K-means adalah Arumanis: 0,70%, Madu: 0,64%, Gedong: 1,00%, Golek: 0,86%. Presentase Accuracy metode klasifikasi K-NN adalah Arumanis: 94,59%, Madu: 89,74%, Gedong: 97,22%, Golek: 92,11% dan metode K-Means adalah Arumanis: 83,78%, Madu: 83,78%, Gedong: 96,88%, Golek: 86,11%. Untuk nilai precision, recall dan accuracy secara global adalah metode K-NN lebih besar daripada metode K-Means. Dengan demikian, metode klasifikasi K-Means yang dimodifikasi untuk dapat mempergunakan data pelatihan (supervised) masih belum mampu sebaik Metode K-NN dalam klasifikasi jenis buah mangga. Diharapkan akurasi metode klasifikasi jenis tanaman buah mangga dengan ekstraksi ciri bentuk dapat memperoleh kualitas bentuk yang seragam.Kata kunci: Pengolahan Citra, Ekstraksi Ciri, K-means, K-NN, Metric, Eccentricity
Co-Authors Agustinus Rudatyo Himamunanto Antonius Bima Murti Wijaya Apriani, Apriani Barita Suci Vernando Zebua Bobby Girsang Bolo, Naomi Tena Budet, Vincensa Woytimena Delima Zai Emerita Setyowati Febe Maedjaja Febe Maedjaja Fornieli Gulo Gallu, Apliana Gogor Christmass Setyawan Gulo, Eniria Heri Wijayanto Hermas Rizzal Himamunanto, A. R. Himamunanto, AR Himamunanto, AR. Ina, Osmanila Tamo Jatmika Jatmika Jatmika Jatmika Jatmika Jatmika Juli Supriyanto Gea Kristian Juri Damai Lase Laia, Yardiana Lase, Anan Sosmita Lase, Kristian J.D. Liefson Jacobus, Liefson Lukas Agung Heriputranto Mendrofa, Milka Justine Nauli, Sumitta Nehe, Pius Hermanto Niasiska, Niasiska Olam, Enos Nikodemus P.Hurit, Lud Gerdus Peter1 Putri, Farelia Devi Julia Rudatyo Himamunanto, Agustinus Setiawan Matangkin, Jimmi Setyawan, Gogor C Sumihar, Yo'el Pieter Sumihar, Yo’el Pieter Sunneng Sandino Berutu Supriadi, Joseph Surjawirawan Dwiputranto Swasono, Natan Enggal Tasane, Elshaddai L Timothy Ageng Pracaya Veren, Yolanda Wanda, Melifan Yeong-Chin Chen Yo’el Pieter Sumihar Zalukhu, Talenta Teholi Zendrato, Agus Dirgahayu Ziliwu, Johni Revormasi