cover
Article Per Year (5 Year)
All
Home Page
OAI Link
Editorial Team
Contact
Reviewer
Google Scholar
Contact Name
Siti Aminah
Contact Email
sitiaminah@ubhinus.ac.id
Phone
+62341-560823
Journal Mail Official
lppm@ubhinus.ac.id
Editorial Address
Jl. Raya Tidar No 100 Malang
Location
Kota malang,
Jawa timur
INDONESIA
Smatika Jurnal : STIKI Informatika Jurnal
Published by Universitas Bhinneka Nusantara
ISSN : 20870256     EISSN : 25806939     DOI : https://doi.org/10.32664/smatika
Core Subject : Science,
Computer Science & IT
SMATIKA: STIKI Informatika Jurnal is a journal published by Lembaga Penelitian & Pengabdian kepada Masyarakat (LPPM) of Universitas Bhinneka Nusantara Malang. The scope of this journal in the field of Computer Science, Information Systems, and Information Management.
Arjuna Subject : Ilmu Komputer (semua kategori) - Sistem Informasi
Articles 258 Documents
 22   23   24   25   26 
Classification of Rice Leaf Diseases Using CNN-SVM Hybrid Model Agus Tri Adiana; Jumadi Jumadi; Eva Nurlatifah
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1548

Abstract

Produksi padi di Indonesia menghadapi tantangan serius akibat berkurangnya luas lahan pertanian dan serangan penyakit seperti Bacterial Leaf Blight, Blast, dan Brown Spot, yang dapat menurunkan hasil panen hingga 80% dan mengancam ketahanan pangan nasional. Penyakit tersebut tidak hanya merusak stabilitas produksi tetapi juga menyebabkan kerugian yang signifikan bagi petani. Identifikasi dini penting untuk mencegah kerugian, namun keterbatasan pengetahuan petani sering menyebabkan kesalahan diagnosis dan penanganan. Untuk mengatasi masalah ini, penelitian ini mengusulkan pengembangan model klasifikasi penyakit daun padi berbasis hybrid Convolutional Neural Network (CNN) dan Support Vector Machine (SVM), yang dirancang menggunakan metode CRISP-DM (Cross-Industry Standard Process for Data Mining) dari tahap business understanding hingga evaluation. Dengan dataset berisi 11.790 gambar daun padi dari sembilan kelas penyakit.CNN menggunakan arsitektur VGG-16 yang dipakai untuk ekstraksi fitur, sedangkan SVM menangani klasifikasi multi-kelas dengan metode one-vs-rest. Hasil evaluasi menunjukkan akurasi model sebesar 95%, dengan precision, recall, dan F1-score yang tinggi di sebagian besar kelas penyakit. Hasil tersebut menunjukkan potensi yang signifikan dan diharapkan dapat membantu petani untuk melakukan deteksi dini penyakit pada padi.
The Analisis Celah Keamanan Website Poltekkes Kemenkes Sorong Menggunakan Metode Penetration Testing Gunawan Yudi Jayanto; Julius Panda Putra Naibaho; Alex De Kweldju
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1549

Abstract

Currently, information technology is very necessary, in the digital era cybersecurity is one of the main factors for an institution to survive and be recognized for its credibility, including educational institutions, the Poltekkes Sorong Website is used as the main portal or media to disseminate academic information, interaction between students and the public, However, the existence of security gaps is something that allows cyber attacks to be carried out which can endanger data confidentiality. This study aims to analyze the security gaps in the Poltekkes Kemenkes Sorong website, the testing carried out using the penetration testing method which has several stages including collecting scanning information, vulnerability analysis. So that this study can provide guidance and practice for information system managers at Poltekkes Kemenkes Sorong to reduce the risk of cyber attacks and protect sensitive data held by the institution.
Klasifikasi Pola Peminjam Buku Bedarsarkan Profesi Menggunakan Algoritma Naïve Bayes Febri Rosita Dewi; Ade Eviyanti; Arif Senja Fitriani; Ika Ratna Indra Astutik
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1661

Abstract

As centers of literacy and learning, libraries face challenges in understanding book lending patterns to meet the needs of diverse users. The main problem faced is the lack of data-based analysis in optimizing library services and collections. This research aims to classify book borrowing patterns based on profession using the Naive Bayes algorithm, utilizing data from the Sidoarjo Library Service in 2023. The data consists of 4476 transactions with attributes such as profession, book category, and level of reading interest. This research was conducted in several phases, namely data collection preprocessing, processing using Gaussian and Multinomial Naive Bayes algorithms, and model evaluation. By testing on various data ratios (90:10, 80:20, 75:25, and 50:50), the results show that Gaussian Naive Bayes provides the highest accuracy of 97% in the random dataset scenario. The main findings show that students, university students and housewives dominate the high reading interest category, while doctors and researchers have lower reading interest. The unique value of this research is in its application of. data-based analysis to support library management. The research results provide strategic insight for developing more responsive data-based services, optimizing collections according to professional needs, and increasing the effectiveness of literacy programs. This research is anticipated to serve as the initial phase in utilizing data mining technology to overcome modern challenges in library management.
Penerapan K-Means dengan Evaluasi Davies-Bouldin Index untuk Pengelompokan Kelas Unggulan SMP Wijaya Sukodono Feny Anggraeny; Ade Eviyanti; Sumarno
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1689

Abstract

This research was conducted at Wijaya Sukodono Middle School, one of the largest schools in Sukodono District which seeks to improve the quality of education by utilizing student academic data. The main objective of this research is to group students based on academic scores using the K-Means Clustering method, which aims to divide students into two categories: Superior Class and Regular Class. The Flagship Class is defined as a group of students with high academic performance, while the Regular Class includes students with lower academic performance. The research method involves collecting report value data, processing, and data transformation, followed by the application of the K-Means algorithm. Evaluation was carried out using the Davies-Bouldin Index (DBI) to assess the quality of clustering. The analysis results show that of the 576 students, 488 students are included in the Superior Class and 88 students are in the Regular Class. The two cluster configuration provides optimal results with a DBI value of 0.337, indicating a good level of inter-cluster certification. This research concludes that the K-Means method is effective in grouping students based on academic performance. These results provide insight into strategies for schools in developing more targeted learning programs to improve the quality of education. Further development can be done by including non-academic variables or exploring other clustering methods for more comprehensive results
Analisis Sentimen Komentar YouTube MV K-Pop Menggunakan Naïve Bayes: Studi Kasus Jung Jaehyun ‘Horizon’ Addriana Fatma Putri Indah Sari; Ade Eviyanti; Ika Ratna Indra Astutik
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1691

Abstract

This research aims to analyze the sentiment of YouTube comments on the music video "Horizon" by Jung Jaehyun by applying the Naïve Bayes and Support Vector Machine (SVM). As a global phenomenon, K-pop serves as an intriguing subject for understanding interaction patterns and fan opinions on social media platforms, particularly YouTube. A total of 2,391 Indonesian-language comments were collected using the YouTube API and processed through preprocessing stages such as data cleaning, tokenization, normalization, and the removal of common stopwords. After manually labeling the comments for positive and negative sentiments, the data was analyzed using the Naïve Bayes algorithm, known for its simplicity, speed, and effectiveness with small datasets, and compared with SVM equipped with a linear kernel. The study found that while SVM with a linear kernel achieved the highest accuracy of 98% and excelled in handling imbalanced data, Naïve Bayes still delivered competitive results with an accuracy of 97%. The advantages of Naïve Bayes, including ease of implementation, computational efficiency, and performance on small datasets, make it an effective choice for similar sentiment analysis cases. Both algorithms demonstrated good performance in predicting sentiments, as shown in their confusion matrices, although challenges persisted with the negative class. This research contributes to sentiment analysis methodologies by highlighting that Naïve Bayes is an efficient and relevant algorithm for preliminary exploration, while SVM is more reliable for performance optimization on complex datasets. The findings are particularly relevant to the music industry in understanding fan sentiment as an indicator of success.
Analisis Sentimen Pengguna Aplikasi Tantan Perbandingan Kinerja Metode Naive Bayes dan SVM Serlindha Tri Andini; Ade Eviyanti; Hamza Setiawan
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1692

Abstract

Tantan, as a popular dating application in Indonesia, has garnered various user reviews reflecting their experiences. This study aims to analyze user sentiment for the Tantan application by comparing the performance of Naive Bayes and Support Vector Machine (SVM) algorithms in sentiment classification. User reviews were collected from Google Play Store using web scraping techniques and processed through data cleaning, tokenization, and TF-IDF feature extraction. The dataset comprises 1,195 reviews, with 74.6% positive and 25.4% negative sentiments. The Naive Bayes model achieved an accuracy of 85.36%, excelling in detecting positive reviews (precision 86%, recall 97%). However, its performance on negative reviews was suboptimal, with a recall of only 44%. Conversely, the SVM model with a sigmoid kernel demonstrated superior overall performance, achieving an accuracy of 87.03%. It handled negative reviews better, with a recall of 67% and an F1-score of 69%, while maintaining excellent results for positive reviews (precision 91%, F1-score 92%). The results indicate that although both algorithms have their strengths, SVM with a sigmoid kernel is recommended for this dataset due to its balanced and stable performance. This model provides valuable insights for feature development and quality improvement strategies for the application.
Implementasi Algoritma BERT untuk Question and Answer System Terkait Hadist dalam Bentuk Virtual Youtuber Moch Arsyil Albany; Ichsan Taufik; Ichsan Budiman
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1704

Abstract

In this digital era, the integration of Islamic teachings with advanced technology has become essential. This research focuses on developing an Islamic QnA system using Artificial Intelligence in the form of a Virtual YouTuber (VTuber). The system leverages the IndoBERT-SQuAD algorithm for Natural Language Processing, particularly in handling questions about hadiths. By employing prototype methodology, the system underwent stages of analysis, design, implementation, and evaluation. Confidence score and F1-score metrics were utilized to assess the system's performance. After contextual grouping, the model demonstrated significant improvement, achieving an F1-score of 0.96875. Despite these advancements, the system still faces challenges in providing accurate long-form answers. This research contributes to the application of technology in Islamic education, offering a practical solution for making hadith knowledge more accessible and appealing to the younger generation.
Sistem Monitoring dan Manajemen Pakan Pakan Ternak Sapi Berbasis Web Pada PT XYZ Lampung Tengah Sania Media Nosa; Heni Sulistiani
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i01.1717

Abstract

This research discusses the design and implementation of a web-based cattle feed monitoring and management system at PT XYZ Lampung Tengah. The main problem faced is the manual recording in cattle feed management, which causes inaccuracies in feed consumption data, incorrect cost calculations, and the risk of inventory shortages or excesses. The research uses the Rapid Application Development (RAD) method in system development, which consists of four phases: Requirements Planning, RAD Design Workshop, Implementation, and Maintenance and Evaluation. The developed system has features for managing user data, feed type data, feed weight recaps, as well as generating recap and stock reports. The implementation results show that the system successfully automated the feed inventory recording process with access rights distribution between admin and user, data visualization through stock and feed expenditure graphs, and the ability to generate organized reports. In making the final report on animal feed, the admin takes up to 7 days because it adjusts warehouse data with feed expenditure records. But with this Web-based monitoring and animal feed system, the admin can present the final report on the use of feed in real-time. This system helps improve operational efficiency and the accuracy of feed usage recording at PT XYZ Lampung Tengah.
Implementasi Algoritma Bidirectional Encoder Representations From Transformer Pada Speech To Text Untuk Notulensi Rapat Abdullah Abdullah; Jumadi; Deden Firdaus
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1725

Abstract

Meeting transcription is a crucial process for organizations, yet it often consumes significant time and resources due to the manual effort involved in recording, understanding, and documenting discussions accurately. In the digital era, advancements in speech processing and natural language understanding provide an opportunity to automate this process. This research focuses on the implementation of the Bidirectional Encoder Representations from Transformers (BERT) algorithm in a Speech-to-Text (STT) system to enhance the accuracy and efficiency of meeting transcriptions. The study integrates BERT, a deep learning-based model capable of comprehending bidirectional contextual information, into the transcription pipeline to improve handling of complex conversational contexts. The research follows a systematic methodology, starting from data preprocessing, model training, and evaluation to assess its performance. Results show that the proposed system achieves high transcription accuracy, demonstrating significant potential for real-world applications in organizational environments. This research also highlights the importance of advanced NLP technologies, such as BERT, in overcoming challenges of transcription in multilingual and noisy environments. The developed system offers practical benefits in terms of reducing manual effort and improving access to meeting documentation, making it a valuable tool for productivity enhancement.
Sistem Pakar Berbasis Web untuk Diagnosis Penyakit Paru Anak dengan Forward Chaining Mochammad Raflie Lazuardi; Ika Ratna Indra Astutik; Ade Eviyanti
SMATIKA JURNAL : STIKI Informatika Jurnal Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal
Publisher : LPPM UBHINUS MALANG

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32664/smatika.v15i02.1738

Abstract

This research aims to design an expert system using the forward chaining method to facilitate the early diagnosis of lung diseases in children, such as tuberculosis, pneumonia, and bronchitis. The system is designed to help the community, especially in areas with limited access to healthcare services, in recognizing symptoms independently. The methodology uses the stages of the Expert System Development Life Cycle (ESDLC), including problem identification, knowledge acquisition from experts, design, and testing using black box techniques. This system is capable of detecting symptoms, matching them with a rule base, and providing an initial diagnosis along with recommended actions. The implementation results show that the system can support quick and accurate medical decision-making, as well as enhance public health awareness through internet-based access.

Page 24 of 26 | Total Record : 258

 22   23   24   25   26 

Filter by Year

2015 2025


Reset
Filter By Issues
All Issue Vol 15 No 02 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 15 No 01 (2025): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 14 No 02 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 14 No 01 (2024): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 13 No 02 (2023): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 13 No 01 (2023): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 12 No 02 (2022): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 12 No 01 (2022): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 11 No 02 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 11 No 01 (2021): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 10 No 02 (2020): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 10 No 01 (2020): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 9 No 02 (2019): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 9 No 01 (2019): SMATIKA Jurnal : STIKI Informatika Jurnal Vol 8 No 02 (2018): SMATIKA Jurnal STIKI Informatika Jurnal Vol 8 No 01 (2018): SMATIKA Jurnal STIKI Informatika Jurnal Vol 7 No 02 (2017): Smatika Jurnal : STIKI Informatika Jurnal Vol 7 No 01 (2017): Smatika Jurnal : STIKI Informatika Jurnal Vol 6 No 02 (2016): Smatika Jurnal : STIKI Informatika Jurnal Vol 6 No 01 (2016): Smatika Jurnal : STIKI Informatika Jurnal Vol 5 No 02 (2015): Smatika Jurnal : STIKI Informatika Jurnal More Issue