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All Journal Jurnal Teknik Informatika UNIKA Santo Thomas J-SAKTI (Jurnal Sains Komputer dan Informatika) bit-Tech MEANS (Media Informasi Analisa dan Sistem) Jurnal Teknologi Informatika dan Komputer J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Teknik Informatika Unika Santo Thomas (JTIUST) Jurnal Media Computer Science
Yustiana, Indra
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Energy

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Analisis Sentimen Pada Ulasan Produk UNIQLO dengan Algoritma Naive Bayes Amelia, Eneng Elsa; Yustiana, Indra
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.773

Abstract

Sentiment analysis is provided by internet users on social media to express personal assessments or opinions. One of the brands that often receives sentiment from users on social media is Uniqlo. Sentiment opinions play a crucial role. In the context of business and information technology, sentiment analysis is often applied to product reviews, customer service, or consumer responses on social media to gather information about how the public perceives a product or brand, which is valuable for both other customers and the store. Currently, the activity of providing product reviews, often referred to as reviews, is gaining attention from many parties and becoming a profession of choice. However, becoming a reviewer requires genuine experience and expertise in the field. This is because reviews, in the form of critiques and suggestions, must be conducted with careful consideration. Those who conduct reviews will adhere to the principles of analysis and facts rather than arbitrary opinions. Reviews, although in the form of concise summaries, can be very useful in various fields, from marketing to the arts. Reviews are a form of evaluation or assessment of a product, service, work of art, book, film, place, or anything else. It involves giving personal opinions or perspectives based on personal experience or knowledge of the subject being reviewed. Reviews can be positive, negative, or neutral depending on the experience, views, or individual perspectives of the reviewer. By using Text Mining classification methods, it is possible to determine whether a sentiment is positive, neutral, or negative. One widely used algorithm in sentiment analysis is the Naïve Bayes classification method.
Analisis Sentimen Pada Ulasan Produk UNIQLO dengan Algoritma Naive Bayes Amelia, Eneng Elsa; Yustiana, Indra
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 8, No 1 (2024): EDISI MARET
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v8i1.773

Abstract

Sentiment analysis is provided by internet users on social media to express personal assessments or opinions. One of the brands that often receives sentiment from users on social media is Uniqlo. Sentiment opinions play a crucial role. In the context of business and information technology, sentiment analysis is often applied to product reviews, customer service, or consumer responses on social media to gather information about how the public perceives a product or brand, which is valuable for both other customers and the store. Currently, the activity of providing product reviews, often referred to as reviews, is gaining attention from many parties and becoming a profession of choice. However, becoming a reviewer requires genuine experience and expertise in the field. This is because reviews, in the form of critiques and suggestions, must be conducted with careful consideration. Those who conduct reviews will adhere to the principles of analysis and facts rather than arbitrary opinions. Reviews, although in the form of concise summaries, can be very useful in various fields, from marketing to the arts. Reviews are a form of evaluation or assessment of a product, service, work of art, book, film, place, or anything else. It involves giving personal opinions or perspectives based on personal experience or knowledge of the subject being reviewed. Reviews can be positive, negative, or neutral depending on the experience, views, or individual perspectives of the reviewer. By using Text Mining classification methods, it is possible to determine whether a sentiment is positive, neutral, or negative. One widely used algorithm in sentiment analysis is the Naïve Bayes classification method.
Rancang Bangun Aplikasi E-Learning Berbasis Progressive Web Apps Untuk Menunjang Pembelajaran Online dengan Metode Prototyping Hardianty, Dini Agnia; Yustiana, Indra; Somantri, S
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.487

Abstract

Covid-19 pandemic has an impact on the Indonesian education system which makes the learning process from offline to online. This is what prompted Hayyatan Thayyibah High School to create an E-Learning application to support the online learning process, apart from being used for online learning, the Hayyatan Thayyibah High School E-learning application was also used to share information and take attendance for reading the Qur'an (Tallaqi). The E-Learning application was built using the Prototyping method, from the results of usability testing, an average score of 80% showed that the application could be used well by students and the teacher.
Rancang Bangun Aplikasi E-Learning Berbasis Progressive Web Apps Untuk Menunjang Pembelajaran Online dengan Metode Prototyping Hardianty, Dini Agnia; Yustiana, Indra; Somantri, S
J-SAKTI (Jurnal Sains Komputer dan Informatika) Vol 6, No 2 (2022): EDISI SEPTEMBER
Publisher : STIKOM Tunas Bangsa Pematangsiantar

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30645/j-sakti.v6i2.487

Abstract

Covid-19 pandemic has an impact on the Indonesian education system which makes the learning process from offline to online. This is what prompted Hayyatan Thayyibah High School to create an E-Learning application to support the online learning process, apart from being used for online learning, the Hayyatan Thayyibah High School E-learning application was also used to share information and take attendance for reading the Qur'an (Tallaqi). The E-Learning application was built using the Prototyping method, from the results of usability testing, an average score of 80% showed that the application could be used well by students and the teacher.
Sistem Keamanan Pintu Rumah dengan Sidik Jari Berbasis Internet Of Things (IOT) Faturrachman, Mirza; Yustiana, Indra
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 2 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.54367/jtiust.v6i2.1517

Abstract

Tingkat kriminalitas yang cukup tinggi yang terjadi di Indonesia disebabkan karena banyaknya faktor, salah satu faktor saat ini yaitu karena adanya virus COVID-19 yang mengakibatkan sektor ekonomi menjadi melemah, banyak orang berbuat jahat demi bisa bertahan hidup. Jenis kejahatan yang terjadi banyaknya adalah kejahatan pencurian, kejahatan tipe ini seringnya terjadi di rumah, dan dari banyak kasus, seringnya pencuri masuk melewati pintu depan rumah. Dengan adanya masalah ini, perlu adanya suatu sistem ataupun alat yang bisa membantu mengatasi masalah ini. Dalam penelitian ini, peneliti merancang suatu sistem keamanan khususnya untuk area pintu depan rumah. Pembuatan sistem keamanan ini berbasis Internet of things (IOT) dengan menggunakan mikrokontroler Arduino, dan menggunakan sensor sidik jari yang digunakan untuk membaca sensor sidik jari pemilik rumah. Penggunaan sensor sidik jari untuk keamanan menurut peneliti dirasa cukup baik, karena setiap orang yang ada di dunia ini memiliki sidik jari yang berbeda dari orang lain. Tujuan dari penelitian ini adalah untuk menghasilkan suatu sistem yang dapat mengurangi resiko pencurian di rumah.
Keamanan Kendaraan untuk Melacak Sepeda Motor yang Hilang dengan menggunakan GPS Berbasis Smartphone Yustiana, Indra; Mulya, Muhamad Satibi
MEANS (Media Informasi Analisa dan Sistem) Volume 6 Nomor 2
Publisher : LPPM UNIKA Santo Thomas Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (760.614 KB) | DOI: 10.54367/means.v6i2.1581

Abstract

Keamanan kendaraan untuk melacak sepeda motor yang hilang merupakan sebuah alat pelacak dengan menggunakan GPS dan di tampilkan dengan smartphone alat ini, berfungsi untuk memberikan informasi lokasi kendaraan, dengan mengirimkan titik koordinat atau lokasi kendaraan yang hilang dengan menggunakan perintah sms yang di kirimkan melalui sebuah smartphone, kemudian bisa di tampilkan dengan aplikasi Google maps. Keamanan kendaraan sepeda motor untuk mengetahui posisi kendaraan yang hilang menggunakan GPS dan ditampilkan dengan smartphone menggunakan alat yaitu : (1) GPS Ublox Neo6m, (2) Modul Arduino uno R3, (3) GSM SIMCOM SIM800L, (4) Smartphone, Adapun perangkat lunak yang di gunakan (1) Aplikasi Google Maps. Perangkat lunak (software) pembuatan alat ini menggunakan Bahasa pemograman C. Diketahui dari hasil pengujian yang telah dilakukan bahwa keamanan kendaraan untuk melacak sepeda motor yang hilang, untuk mengetahui posisi kendaraan yang hilang menggunakan GPS dan ditampilkan dengan smartphone hasil dari pengujian dapat disimpulkan bahwa alat tersebut bisa menampilkan data informasi sesuai dengan yang di harapkan yaitu dengan menggunakan alat tersebut dengan menggunakan perintah sms pada sebuah smartphone untuk mengirimkan lokasi atau titik koordinat sepeda motor ke smartphone pemilik kendaraan. Kesimpulan yang di dapatkan dari alat ini adalah alat ini dapat di gunakan dan sesuai dengan yang diharapkan.
Sistem Keamanan Pintu Rumah dengan Sidik Jari Berbasis Internet Of Things (IOT) Faturrachman, Mirza; Yustiana, Indra
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 6 No. 2 : Tahun 2021
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (532.29 KB) | DOI: 10.54367/jtiust.v6i2.1517

Abstract

Tingkat kriminalitas yang cukup tinggi yang terjadi di Indonesia disebabkan karena banyaknya faktor, salah satu faktor saat ini yaitu karena adanya virus COVID-19 yang mengakibatkan sektor ekonomi menjadi melemah, banyak orang berbuat jahat demi bisa bertahan hidup. Jenis kejahatan yang terjadi banyaknya adalah kejahatan pencurian, kejahatan tipe ini seringnya terjadi di rumah, dan dari banyak kasus, seringnya pencuri masuk melewati pintu depan rumah. Dengan adanya masalah ini, perlu adanya suatu sistem ataupun alat yang bisa membantu mengatasi masalah ini. Dalam penelitian ini, peneliti merancang suatu sistem keamanan khususnya untuk area pintu depan rumah. Pembuatan sistem keamanan ini berbasis Internet of things (IOT) dengan menggunakan mikrokontroler Arduino, dan menggunakan sensor sidik jari yang digunakan untuk membaca sensor sidik jari pemilik rumah. Penggunaan sensor sidik jari untuk keamanan menurut peneliti dirasa cukup baik, karena setiap orang yang ada di dunia ini memiliki sidik jari yang berbeda dari orang lain. Tujuan dari penelitian ini adalah untuk menghasilkan suatu sistem yang dapat mengurangi resiko pencurian di rumah.
Application Of Vision Transformer For Identifying Indonesian Herbal Plants Based On Visual Images Sanjaya, Imam; Lelita, Tiara; Yustiana, Indra
Jurnal Media Computer Science Vol 4 No 2 (2025): Juli
Publisher : LPPJPHKI Universitas Dehasen Bengkulu

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37676/jmcs.v4i2.8896

Abstract

Indonesia has vast biodiversity, including herbal plants that have been used for generations as traditional medicinal ingredients. However, the many types of herbal plants that have similar shapes, colors, and textures often make it difficult for people to identify them accurately. To overcome this challenge, this research develops a visual image-based herbal plant identification system using the Vision Transformer (ViT) model, an artificial intelligence approach that is able to understand visual patterns more effectively than conventional methods. This research went through several stages, including the collection of herbal plant image datasets from public platforms, data preprocessing and image dimension adjustment, and training of the ViT model. The model was evaluated using metrics such as accuracy, precision, recall, and F1-score to ensure optimal performance. The results show that the ViT model is able to identify herbal plants with an accuracy of 92% and consistent performance of other evaluation metrics. This system is also implemented into the web, thus helping users in recognizing herbal plants quickly and accurately
Application of YOLOv8 Model for Early Detection of Diseases in Bean Leaves Yustiana, Indra; Sujjada, Alun; Tirawati
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2514

Abstract

Bean plant is one of the high economic value horticultural commodities widely cultivated in Indonesia. However, its productivity declines due to pest attacks and leaf diseases. Farmers' limitations in accurately identifying disease types also pose obstacles in early mitigation efforts. Therefore, technology-based solutions capable of quickly and accurately detecting plant diseases are needed. This research aims to develop and evaluate the performance of a leaf disease detection model for bean plants using the You Only Look Once version 8 (YOLOv8) algorithm with a transfer learning approach. The dataset used consists of 1,037 images of bean leaves, classified into three categories: angular leaf spots, leaf rust, and healthy leaves. Data were obtained from two sources, namely field documentation in Sindang Village, Sukabumi Regency, and an open repository on GitHub. The dataset was divided into training data (70%), validation (20%), and testing (10%). The model was trained using the YOLOv8s architecture for 30 epochs and achieved a detection accuracy of 85%. Performance evaluation was conducted using precision, recall, and mean average precision (mAP) metrics. The results of this study are expected to be an initial contribution to the application of artificial intelligence in agriculture, particularly in helping farmers efficiently detect leaf diseases in beans to improve productivity and quality of harvest.
Implementation of Machine Learning Using Decision Tree Method for Social Assistance Recipient Classification Perhan, Akbar Ilham; Yustiana, Indra; Sanjaya, Imam
bit-Tech Vol. 8 No. 1 (2025): bit-Tech
Publisher : Komunitas Dosen Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32877/bt.v8i1.2755

Abstract

The distribution of social assistance in Indonesia often faces challenges in accuracy, where individuals who are financially capable still receive aid, while those truly in need are excluded. To address this issue, this study applies a Machine Learning approach using the C4.5 Decision Tree algorithm to classify the eligibility of recipients in Bojonggenteng Village. This algorithm was chosen because it is easy to interpret, performs well, and is suitable for categorical data. The main objective of the study is to develop a classification model that enhances the objectivity and accuracy in determining aid recipients, ensuring that assistance is directed to those who truly need it. The research process involves several stages, including problem identification, literature review, data collection, preprocessing, classification, and model evaluation. A total of 904 records from the 2023 BPNT and PBI-JK programs were obtained in collaboration with the local village authorities. The classification process was conducted using RapidMiner, which allows for visual data processing and model building without requiring programming. The model evaluation was carried out using a confusion matrix, yielding an accuracy of 98.90%, precision of 100%, recall of 97.60%, and an AUC score of 0.988. These results indicate that the C4.5 algorithm is effective for prediction tasks and can be a valuable tool in supporting fair and data-driven decision-making in social assistance programs. This study concludes that the application of Machine Learning in this context improves the fairness and transparency of aid distribution and recommends future research to involve larger datasets for broader implementation.
Co-Authors Alun Sujjada Amelia, Eneng Elsa Faturrachman, Mirza Hardianty, Dini Agnia Insany, Gina Purnama Kharisma, Ivana Lucia Lelita, Tiara Mulya, Muhamad Satibi Perhan, Akbar Ilham Putri, Azzahra Sanjaya, Imam Somantri, S Tirawati Zahra, Falya Amrina