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All Journal Jurnal Masyarakat Informatika Jurnal Simetris Scientific Journal of Informatics Proceeding SENDI_U Jurnal Terapan Abdimas Sistemasi: Jurnal Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control JTERA (Jurnal Teknologi Rekayasa) EDUMATIC: Jurnal Pendidikan Informatika Building of Informatics, Technology and Science Abdimasku : Jurnal Pengabdian Masyarakat Buletin Poltanesa Advance Sustainable Science, Engineering and Technology (ASSET) Jurnal Riset dan Aplikasi Mahasiswa Informatika (JRAMI) Community: Jurnal Pengabdian Pada Masyarakat Jurnal Informatika: Jurnal Pengembangan IT LogicLink: Journal of Artificial Intelligence and Multimedia in Informatics
Astuti, Yani Parti
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Agriculture, Biological Sciences & Forestry Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Health Professions Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Social Sciences Other

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Development of a Patient Safety Incident Reporting System using the Agile Development Method Anam, Muhammad Khoirul; Astuti, Yani Parti; Mubarok, Ahmad Hasan
Sistemasi: Jurnal Sistem Informasi Vol 14, No 5 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i5.5280

Abstract

One of the critical aspects of healthcare services is patient safety. However, the reporting of patient safety incidents in Indonesia remains low due to the use of inefficient manual systems and the limited participation of healthcare professionals. This study aims to develop a web-based patient safety incident reporting system using the Agile Development method. Agile was chosen for its ability to progressively adapt to user needs through fixed-duration iterations (sprints). The uniqueness of the system lies in the integration of the Agile approach with the Laravel architecture, which enables rapid, modular, and participatory development. Data were collected through interviews, questionnaires, and literature reviews, and analyzed using the PIECES framework. The system was developed using the Laravel framework and evaluated through User Acceptance Testing (UAT) with 24 respondents from the Temanggung District General Hospital (RSUD Kabupaten Temanggung). The testing results showed that 91% of respondents strongly agreed on the system's ease of use and effectiveness. The system has proven to enhance efficiency, accuracy, and user engagement in the incident reporting process. It also offers practical implications for other hospitals aiming to build more integrated and adaptive reporting systems to support improved healthcare service quality.
PENDAMPINGAN GURU – GURU DALAM PEMBUATAN RAPORT KURIKULUM 2013 DI MI MIFTAHUL HIDAYAH GUNUNGPATI SEMARANG Astuti, Yani Parti; Subhiyakto, Egia Rosi; Adi, Prajanto Wahyu
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 2, No 2 (2019): Juli 2019
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (720.989 KB) | DOI: 10.33633/ja.v2i2.44

Abstract

Sistem Pendidikan Indonesia sering berubah – ubah berdasarkan perkembangan teknologi di Indonesia. Hal ini dibuktikan dengan penerapan kurikulum 2013 pada setiap sekolah baik SD/MI sampai jenjang SMA/SMK. Pada kurikulum 2013 terjadi perubahan baik dalam materi maupun administrasi. Di bidang materi setiap guru bisa mempersiapkan dengan baik dan terpercaya. Namun untuk segi administrasi diperlukan waktu untuk mempelajarinya khususnya dalam pembuatan raport. Untuk itu, maka dinas pendidikan memberikan sistem yang dipelajari oleh setiap guru yang sudah menggunakan kurikulum 2013. Sehingga perlu diadakan pendampingan agar dalam pembuatan raport kurikulum 2013 bisa direalisasikan. Sebagai hasil dari pendampingan ini, maka setiap guru bisa membuat raport kurikulum 2013 dengan baik dan benar. 
Lung Cancer Classification using the Naïve Bayes Method with SMOTE Akbar, Ananda Ikhwana Khairur; Astuti, Yani Parti
Sistemasi: Jurnal Sistem Informasi Vol 14, No 6 (2025): Sistemasi: Jurnal Sistem Informasi
Publisher : Program Studi Sistem Informasi Fakultas Teknik dan Ilmu Komputer

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32520/stmsi.v14i6.5607

Abstract

The primary challenges addressed in this study include delays in the early detection of lung cancer due to non-specific initial symptoms, the limitations of the Naïve Bayes algorithm in processing categorical data such as symptoms, gender, and smoking habits, as well as class imbalance issues in the dataset that can affect model accuracy. To overcome these challenges, the SMOTE (Synthetic Minority Over-sampling Technique) method was applied to improve classification performance. This study aims to implement the Naïve Bayes algorithm for lung cancer classification and compare its performance on imbalanced data versus data balanced using SMOTE. The methodology consists of data preprocessing, encoding, applying SMOTE for balancing, and classification using Naïve Bayes. Evaluation was performed using three data split ratios: 80:20, 70:30, and 60:40. The results show that applying SMOTE led to performance improvements, with the most significant gains observed at the 60:40 split ratio. In this case, model accuracy improved from 88.29% to 93.19%. For the “Yes” (positive) class, precision remained at 0.96, recall at 0.91, and F1-score at 0.93. However, for the “No” (negative) class, precision improved from 0.40 to 0.90, recall from 0.60 to 0.96, and F1-score from 0.48 to 0.93. Conversely, slight decreases in accuracy were observed for the 80:20 and 70:30 ratios after SMOTE application. These findings demonstrate that SMOTE significantly enhances model performance at the 60:40 ratio, not only in terms of accuracy but also in recall and F1-score, which are crucial for reducing false negatives in the minority (“Yes”) class. This is especially critical in early detection, as correctly identifying actual cancer cases is more important than merely maintaining overall accuracy. Although SMOTE did not always improve accuracy at other ratios, it still contributed to better cancer case detection. Therefore, its application should be considered carefully, balancing overall accuracy with clinically meaningful metrics.
Penerapan Metode Naïve Bayes Classifier Untuk Klasifikasi Sentimen Pada Judul Berita Astuti, Yani Parti; Wibowo, Alrico Rizki; Kartikadarma, Etika; Subhiyakto, Egia Rosi; Sri Winarsih, Nurul Anisa; Rohman, Muhammad Syaifur
LogicLink Vol. 1 No. 1, June 2024
Publisher : Universitas Islam Negeri K.H. Abdurrahman Wahid Pekalongan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28918/logiclink.v1i1.7684

Abstract

News has a major role as a source of information to convey reports on opinions, events, and the latest findings in various aspects of life. News headlines, as an important component, can be a determinant of news content. The sentiment contained in news headlines can be classified using sentiment analysts, as is the case in the online media platform Kompas.TV. News headlines are retrieved using an automated program that utilises the HTML body with the help of NodeJs as the technology for program creation. This research is focused on the application of Naïve Bayes Classifier method to classify sentiment on Kompas.TV news headlines in Semarang City. The results showed an accuracy rate of 91.04%, with a ratio of training data and test data of 90:10. The conclusion of this study is that the Naïve Bayes Classifier method is effective in identifying news headlines with negative sentiment on Kompas.TV, with a precision of 89% and recall of 94%. This finding makes a positive contribution to the understanding of sentiment analysis on news headlines in online media, especially in the context of Kompas.TV news in Semarang City.
Stroke Classification Comparison with KNN through Standardization and Normalization Techniques Firmansyah, Muhammad Raihan; Astuti, Yani Parti
Advance Sustainable Science, Engineering and Technology Vol 6, No 1 (2024): November-January
Publisher : Universitas PGRI Semarang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26877/asset.v6i1.17685

Abstract

This study explores the impact of z-score standardization and min-max normalization on K-Nearest Neighbors (KNN) classification for strokes. Focused on managing diverse scales in health attributes within the stroke dataset, the research aims to improve classification model accuracy and reliability. Preprocessing involves z-score standardization, min-max normalization, and no data scaling. The KNN model is trained and evaluated using various methods. Results reveal comparable performance between z-score standardization and min-max normalization, with slight variations across data split ratios. Demonstrating the importance of data scaling, both z-score and min-max achieve 95.07% accuracy. Notably, normalization averages a higher accuracy (94.25%) than standardization (94.21%), highlighting the critical role of data scaling for robust machine learning performance and informed health decisions.
Pendampingan Pembuatan Video Animasi untuk Siswa SMA At Thohiriyyah Semarang Astuti, Yani Parti; Utomo, Danang Wahyu; Sudibyo, Usman; Fahmi, Amiq; Kartikadarma, Etika; Dolphina, Erlin; Subhiyakto, Egia Rosi
Community : Jurnal Pengabdian Pada Masyarakat Vol. 4 No. 3 (2024): November : Jurnal Pengabdian Pada Masyarakat
Publisher : LPPM Sekolah Tinggi Ilmu Ekonomi - Studi Ekonomi Modern

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/3s4ejr82

Abstract

Information technology has developed and provided progress such as the increasing use of computers and the internet in the world, especially in the world of education. Through computers and the internet, all information can be disseminated and can be used as learning materials for students. The development of information technology and the internet, not all information is disseminated positively. Some information is disseminated negatively such as fake news (hoaxes), radicalism, and hate speech. There needs to be skills in using the development of information technology. Digital literacy trains users not only to be proficient in using information technology but also to have the ability to think critically, creatively, and innovatively to produce digital competence. SMA At Thohiriyyah is one of the high schools in Semarang that focuses on understanding and improving the abilities of its students in digital literacy. Insight is needed for SMA At Thohiriyyah students in understanding the importance of digital literacy. Animation video training is one way to increase student creativity in digital literacy in creating learning videos. With this training, it is hoped that students can develop learning videos that can be used on social media such as YouTube
Perbandingan Naive Bayes dan Support Vector Machine dalam Klasifikasi Tingkat Kemiskinan di Indonesia Mukharyahya, Zulfa Alviandri; Astuti, Yani Parti; Cahyani, Okta Nur
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29512

Abstract

Poverty in Indonesia is a complex issue influenced by various economic and socio-cultural factors. This study aims to compare the performance of Naïve Bayes and Support Vector Machine (SVM) in classifying poverty levels in Indonesia while also evaluating the effectiveness of random oversampling in addressing data imbalance. The dataset consists of 514 samples from various districts and cities in Indonesia, with 452 samples classified as "not poor" and 62 as "poor." After applying oversampling, the total number of samples increased to 730, with a balanced distribution (365 samples per class). The observed data include socio-economic indicators such as the percentage of the poor population, per capita expenditure, the Human Development Index, and the open unemployment rate. The study splits the data using an 80:20 ratio for training and testing. Experimental results show that SVM achieved a higher accuracy of 81% compared to naïve bayes, which reached 76%. Additionally, SVM demonstrated a more stable balance between precision and recall. On the other hand, the oversampling technique effectively improved the model’s ability to identify the minority class, particularly for Naïve Bayes, which was more responsive to data duplication. These findings highlight the role of machine learning in designing more effective social policies for poverty data management.
Prediksi Penjualan Tanaman Hias menggunakan Regresi Linier Berganda dengan Perbandingan Eliminasi Gauss dan Cramer Bagaswara, Dwiky Ihza; Astuti, Yani Parti
Jurnal Pendidikan Informatika (EDUMATIC) Vol 9 No 1 (2025): Edumatic: Jurnal Pendidikan Informatika
Publisher : Universitas Hamzanwadi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29408/edumatic.v9i1.29627

Abstract

Sales prediction is a crucial element in the ornamental plant business to support inventory planning and marketing strategies. Our research aims to compare the Gauss and Cramer elimination methods (determinant matrix) in multiple linear regression to assess the accuracy of sales prediction. Gauss elimination is effective for systems of large size, while the Cramer method is more consistent in handling systems of linear equations that have correlated variables. The dataset used consists of 212 data points, including unit price as the dependent variable and stock, quantity sold, and total revenue as the independent variables. The accuracy was compared using Mean Absolute Percentage Error (MAPE) due to its ability to measure the error relative to the true value. Our findings show that the Cramer method has a MAPE of 21%, which is lower than Gauss elimination with a MAPE of 40%, making it more accurate in sales prediction. With a more precise method, business owners can optimize inventory management, set prices more efficiently, and devise data-driven marketing strategies. Our results also provide insights for other sectors that use predictive analytics to improve business decision-making.
Pendampingan Penggunaan Media Pembelajaran Game Edukasi "Code.org" bagi Siswa SMP Ibu Kartini Semarang Astuti, Yani Parti; Subhiyakto, Egia Rosi; Umaroh, Liya; Sutojo, Totok; Supriyanto, Catur
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 7, No 1 (2024): JANUARI 2024
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62411/ja.v7i1.1799

Abstract

Transformasi pendidikan lewat kebijakan merdeka belajar adalah perwujudan demi mewujudkan SDM Unggul Indonesia yang memiliki Profil Pelajar Pancasila. Merdeka belajar ditujukan untuk jenjang pendidikan dasar dan pendidikan menengah seperti SMP/SMA/SMK/Sederajat. Jenjang SMP yang pada kurikulum 2013 tidak ada mata pelajaran TIK, tapi pada saat akan masuk SMA dituntut untuk bisa materi TIK secara dasar. Untuk itu tim pengabdian Udinus menawarkan adanya suatu kerja sama yang intinya memberi pelatihan dan pendampingan kepada siswa – siswa SMP Ibu Kartini belajar materi tentang TIK. Agar materi yang akan diberikan tidak membosankan, maka materi tersebut akan diambil tema game edukasi. Banyak geme edukasi yang tersebar di dunia teknologi saat ini, maka tim pengabdi memilih metode yang sesuai dengan siswa SMP dan bisa menjadi bekal siswa – siswa tersebut dalam menghadapi program merdeka belajar saat SMA nanti. Metode yang akan diambil adalah metode pemahaman logika dalam pemrograman computer yang ada pada game edukasi code.org. Dalam game tersebut siswa bisa mengerjakan 20 game yang tingkat kesulitannya berdasarkan logika pemrograman computer yang nantinya akan dipelajari di SMA. Dengan pelatihan game edukasi ini, siswa bisa benar – benar mengerti tentang logika pemrograman. Pada akhir pelatihan ini, siswa akan mendapatkan sertifikat dari code.org bila bisa menyelesaikan 20 game dengan benar.
Deteksi Malware menggunakan Metode Stacking berbasis Ensemble Rafrastara, Fauzi Adi; Supriyanto, Catur; Paramita, Cinantya; Astuti, Yani Parti
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 1 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i1.4606

Abstract

Serangan malware kian hari kian memprihatinkan. Evolusi malware yang cepat dan semakin destruktif menimbulkan kekhawatiran bagi banyak pihak. Oleh karena itu, deteksi malware yang efektif sangat dibutuhkan. Data mining memainkan peran yang krusial dalam bidang ini, mengingat algoritma-algoritma yang ada pada data mining bisa dilatih hingga menghasilkan akurasi yang paling tinggi. Untuk mengklasifikasi suatu file, apakah tergolong malware atau tidak, dalam penelitian ini metode stacking digunakan karena dapat meningkatkan akurasi jika dibandingkan dengan algoritma-algoritma klasifikasi konvensional. Empat Algoritma dilibatkan dalam eksperimen yang dilakukan, yaitu: Neural Network, Random Forest, kNN, dan Logistic Regression. Tiga algoritma pertama digunakan sebagai classifier pada level 0, sementara itu Logistic Regression digunakan classifier pada level 1 (meta classifier).  Dengan kombinasi 4 algoritma tersebut, akurasi yang diperoleh adalah sebesar 98.7%, dan akurasi tersebut merupakan yang paling tinggi jika dibandingkan dengan masing-masing algoritma jika dieksekusi secara individual.
Co-Authors Abu Salam Achmad Wahid Kurniawan Adhitya Nugraha Agus Winarno, Agus Ahmed, Foez Akbar, Ananda Ikhwana Khairur Anam, Muhammad Khoirul Andreas Wilson Setiawan Ardianto, Luluk Ardytha Luthfiarta Bagaswara, Dwiky Ihza Cahyani, Okta Nur Catur Supriyanto Christy Atika Sari D, Ishak Bintang Darmawan, Aditya Aqil Egia Rosi Subhiyakto, Egia Rosi Eko Hari Rachmawanto Erlin Dolphina Erna Zuni Astuti Erwin Yudi Hidayat Etika Kartikadarma Fahmi Amiq Fauzi Adi Rafrastara Firmansyah, Muhammad Raihan Guruh Fajar Shidik Hafidhoh, Nisa'ul Harisa, Ardiawan Bagus Heribertus Himawan Ifan Rizqa Isinkaye, Folasade Olubusola Iskandar, Marcelino Liya Umaroh, Liya Miyanthi, Sheryn Aulia Mubarok, Ahmad Hasan Mukharyahya, Zulfa Alviandri Ningrum, Novita Kurnia Nova Rijati Nugroho, Setiawan Octaviani, Dhita Aulia Oktaridha, Harwinanda Paramita, Cinantya Pradana, Rendy Risqi Prajanto Wahyu Adi, Prajanto Wahyu Pratama, Zahwa Bunga Putri Purwanto Purwanto Rahadian, Arief Rohman, Muhammad Syaifur Sausan Claudia, Salwa Rona Septiani, Karlina Dwi Sindhu Rakasiwi Sudibyo, Usman Sugiyanto - Swanny Trikajanti Widyaatmadja T. Sutojo Triginandri, Rifqi Utomo, Danang Wahyu Wardatunizza, Indah Wibowo, Alrico Rizki Winarsih, Nurul Anisa Sri Yuniarsi Rahayu