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All Journal Jurnal Teknologi Informasi dan Ilmu Komputer Sistemasi: Jurnal Sistem Informasi Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control PROCESSOR Jurnal Ilmiah Sistem Informasi, Teknologi Informasi dan Sistem Komputer Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Bubungan Tinggi: Jurnal Pengabdian Masyarakat Jurnal Nasional Teknik Elektro dan Teknologi Informasi Jurnal Pengabdian Masyarakat Duta Abdimas: Jurnal Pengabdian Masyarakat JuTISI (Jurnal Teknik Informatika dan Sistem Informasi)
Fitri Ayuning Tyas
STMIK Muhammadiyah Paguyangan Brebes
Humanities Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Public Health Social Sciences Other

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Optimalisasi fitur slide master dan hyperlink Ms. PowerPoint dalam pembuatan media presentasi bagi siswa Tyas, Fitri Ayuning; Alifiani, Intan; Abdillah, Muhammad Aznar
Jurnal Inovasi Hasil Pengabdian Masyarakat (JIPEMAS) Vol 5 No 3 (2022)
Publisher : University of Islam Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33474/jipemas.v5i3.14749

Abstract

Ms. PowerPoint merupakan aplikasi pengolah presentasi yang banyak digunakan dalam pembuatan bahan ajar bagi guru maupun sebagai tugas presentasi bagi siswa. Saat ini Ms. PowerPoint termasuk dalam materi komputer dasar yang tidak diajarkan sebagai mata pelajaran wajib pada kurikulum SMK jurusan RPL, melainkan terintegrasi dengan mata pelajaran lain. Siswa dianggap telah mahir mengoptimalkan fitur-fitur Ms. PowerPoint seperti slide master dan hyperlink. Kendati demikian, anggapan ini tidak sesuai dengan hasil pre test yang dilakukan. Data hasil pre test menunjukkan 89% siswa menggunakan Ms. Power Point sebagai aplikasi pengolah presentasi, namun hanya 36% siswa yang memiliki pengetahuan pemanfaatan fitur-fitur Ms. PowerPoint. Menyikapi hal tersebut tim pelaksana Pengabdian kepada Masyarakat (PkM) STMIK Muhammadiyah Paguyangan Brebes melakukan kegiatan pelatihan dengan tujuan melatih siswa membuat media presentasi dengan mengoptimalkan fitur slide master dan hyperlink. Hasil ketercapaian kegiatan pelatihan berdasarkan analisis pre test dan post test menggunakan metode Gain Scores membuktikan bahwa sebanyak 75% siswa mengalami peningkatan kemampuan dengan kriteria tinggi dan 25% siswa dengan kriteria sedang.
Application Rule Base on Facial Skin Type Identification Expert System using Forward Chaining Basir, Azhar; Tyas, Fitri Ayuning; Maghsyari, Yusril Ahzam
Sistemasi: Jurnal Sistem Informasi Vol 13, No 4 (2024): 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.v13i4.4071

Abstract

Most people, especially women, have a great desire to have white, healthy, clean and well-maintained facial skin. However, their knowledge about facial skin types is still limited, even though consulting with an expert requires a lot of time and money which results in someone not paying attention to facial skin type when carrying out treatment. Therefore, an expert system is needed that can help identify facial skin types. A rule base is a rule created based on expert knowledge needed to create an expert system. The forward chaining method is a search method or forward tracing technique that starts from existing information and combines rules to produce a conclusion or goal. The research results show that this application can run well and is suitable for use. Based on the results of system testing from an expert, it was concluded that identifying facial skin types based on facial skin criteria using the forward chaining method had an accuracy rate of 84% where the results of system testing produced several conclusions about the appropriate type of facial skin with the selected criteria data.
Prediksi Resiko Penggunaan Media Sosial Terhadap Kesehatan Mental Menggunakan Exploratory Data Analysis (EDA) dan Cross Industry Standard Process For Data Mining (CRISP-DM) Ayuning Tyas, Fitri; Basir, Azhar; Ardhin, Amira Elistya
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 13 No 2: April 2026
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2026133

Abstract

Media sosial telah menjadi bagian penting dalam kehidupan masyarakat, namun peningkatan penggunaannya sering dikaitkan dengan dampak negatif terhadap kesehatan mental seperti stres, adiksi, FoMo, dan insomnia. Upaya prediksi risiko penggunaan media sosial dapat membantu menjaga kesehatan mental dengan memanfaatkan teknik data mining. Penelitian ini menggunakan metodologi CRISP-DM sebagai kerangka utama serta Exploratory Data Analysis (EDA) untuk mengidentifikasi tren dan anomali yang mendukung proses pemodelan. Beberapa algoritma supervised learning seperti C4.5, k-NN, dan Naïve Bayes diterapkan untuk memprediksi dampak negatif penggunaan media sosial terhadap kesehatan mental. Hasil eksperimen menunjukkan bahwa Naïve Bayes memberikan kinerja terbaik dengan akurasi tertinggi sebesar 92,5%, melampaui C4.5 dan k-NN. Integrasi EDA dan CRISP-DM terbukti menghasilkan model prediksi yang akurat, meskipun penerapan EDA memerlukan waktu tambahan dalam analisis. CRISP-DM berperan penting dalam menyediakan kerangka kerja yang sistematis sehingga membantu peneliti bekerja lebih terstruktur dan mengurangi risiko kesalahan. Selain itu, temuan memperlihatkan bahwa semakin lama seseorang menggunakan media sosial, semakin besar dampak negatif yang dialami, terutama bagi mereka yang menghabiskan waktu lebih dari lima jam per hari. Secara keseluruhan, hasil penelitian ini memberikan kontribusi terhadap pengembangan model prediksi berbasis data mining dan dapat menjadi landasan bagi upaya pencegahan gangguan kesehatan mental akibat penggunaan media sosial.   Abstract Social media has become an integral part of modern life, enabling users to express feelings and opinions. However, its increasing use has been linked to negative impacts on mental health, such as stress, addiction, FoMo, and insomnia. Predicting the risks associated with social media use can help maintain mental well-being, and this can be achieved through data mining techniques. This study applies the CRISP-DM methodology as the main framework, complemented by Exploratory Data Analysis (EDA) to identify trends and anomalies that support the modeling process. Several supervised learning algorithms, including C4.5, k-NN, and Naïve Bayes, were employed to predict the negative impact of social media use on mental health. Experimental results show that Naïve Bayes achieved the best performance with the highest accuracy of 92.5%, outperforming both C4.5 and k-NN. The integration of EDA and CRISP-DM proved effective in producing accurate predictive models, although EDA required additional time for data analysis. CRISP-DM played a crucial role in providing a systematic framework, enabling researchers to work more structurally and minimizing the risk of errors. Furthermore, findings indicate that the longer individuals spend on social media, the greater the negative impact they experience, particularly among those using it for more than five hours per day. Overall, this study contributes to the development of predictive models based on data mining and provides insights that may support preventive efforts against mental health issues caused by excessive social media use.
Peningkatan Performa Classification and Regression Tree Menggunakan Bagging pada Diagnosis Penyakit Jantung Fitriyana, Kokom Hera; Tyas, Fitri Ayuning; Jamil, Abdul
Jurnal Teknik Informatika dan Sistem Informasi Vol 12 No 1 (2026): JuTISI
Publisher : Maranatha University Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28932/jutisi.v12i1.12439

Abstract

Heart disease is one of the leading causes of death worldwide, necessitating fast and accurate diagnostic methods for effective prevention. One approach that can be used is data mining, particularly classification methods to analyze health data. The Classification and Regression Tree (CART) algorithm is known for its interpretability but has a drawback in terms of model stability against data variation. To address this issue, the Bootstrap Aggregating (Bagging) technique is applied to improve the model’s stability and accuracy. This study aims to implement and evaluate the effectiveness of the Bagging technique in enhancing the performance of the CART algorithm for heart disease diagnosis. The data used in this study consists of three datasets available on the Kaggle platform: Heart Disease, Heart Disease Cleveland, and Heart Disease Prediction. The model is built under two conditions: using default parameters and using parameters optimized through the Grid Search method. The research process includes data preprocessing (data type adjustment, handling missing values, and outlier detection), training of two types of classification models (single CART and CART with Bagging), and evaluation based on accuracy metrics. The results show that the application of the Bagging technique consistently improves the accuracy of the CART algorithm. Under default parameters, accuracy increased from 72.89% to 78% (Heart Disease), 81.89% to 85.78% (Heart Disease Cleveland), and 77.44% to 82.44% (Heart Disease Prediction). With tuned parameters, accuracy increased from 75% to 84% (Heart Disease), 77% to 83% (Heart Disease Cleveland), and remained at 83% (Heart Disease Prediction). Therefore, the Bagging technique is proven effective in enhancing the accuracy and stability of the CART model for heart disease diagnosis.
Co-Authors Abdillah, Muhammad Aznar Abdul Jamil Ahmad Faizin, Ahmad Alifiani, Intan Ardhin, Amira Elistya Barokah, Laelatul Basir, Azhar Fadilatul Fajriyah, Rizqi Fitriyana, Kokom Hera Ilham, Ahmad Intan Alifiani Lorenza, Fadiya Olivia Maghsyari, Yusril Ahzam Mahda Nurayuni Mamur Setianama Muhammad Aznar Abdillah Pahlevi, Ryan Fitrian Rakhmawati, Hidayatur Setianama, Mamur