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All Journal Jurnal Ilmu Komputer Pixel : Jurnal Ilmiah Komputer Grafis Elkom: Jurnal Elektronika dan Komputer CommIT (Communication & Information Technology) Jurnal Transformatika RABIT: Jurnal Teknologi dan Sistem Informasi Univrab Jurnal Komtika (Komputasi dan Informatika) Jiko (Jurnal Informatika dan komputer) Jurnal Informasi dan Komputer Jurnal Pengabdian UNDIKMA LOSARI: Jurnal Pengabdian Kepada Masyarakat Jurnal Pengabdian Masyarakat Nusantara Jurnal Abdi Masyarakat Indonesia Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Bulletin of Information Technology (BIT) Jurnal Inovasi Pengabdian dan Pemberdayaan Masyarakat SmartComp Jurnal Pengabdian Masyarakat Tekno Jurnal Komtika (Komputasi dan Informatika)
Fari Katul Fikriah
Program Studi Sistem Dan Teknologi Informasi, Fakultas Keperawatan, Bisnis Dan Teknologi, Universitas Widya Husada, Indonesia
Religion Agriculture, Biological Sciences & Forestry Humanities Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Health Professions Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Mathematics Nursing Public Health Social Sciences Transportation Other

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Emotion Detection Using Contextual Embeddings for Indonesian Product Review Texts on E-commerce Platform Ariyanto, Amelia Devi Putri; Fari Katul Fikriah; Arif Fitra Setyawan
Pixel :Jurnal Ilmiah Komputer Grafis Vol. 17 No. 1 (2024): Pixel :Jurnal Ilmiah Komputer Grafis dan Ilmu Komputer
Publisher : UNIVERSITAS STEKOM

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/pixel.v17i1.2010

Abstract

The advancement of e-commerce has changed the way people shop. However, there is a mismatch between the actual quality of a product and the seller’s description. Product reviews are an important source of information for making purchasing decisions. However, processing large numbers of reviews manually is difficult. This research aims to detect emotions in Indonesian language product review texts using contextual embeddings. The public dataset used was PRDECT-ID, which comprises five emotion labels. The methods used include data preprocessing, feature extraction using contextual embeddings such as Bidirectional Encoder Representations from Transformers (BERT), and classification using Decision Tree, Naïve Bayes, and k-Nearest Neighbors (KNN). Among the compared models, the KNN model demonstrated the highest improvement, achieving a 15.09% enhancement over the decision tree results. This research provides insights into the effectiveness of contextual embeddings in detecting emotions in Indonesian language product review texts.
Pelatihan Desain Grafis Siswa PKBM Bangkit di Kota Semarang untuk Kemandirian Ekonomi Kreatif Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul
Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat Vol. 5 No. 5 (2025): September 2025 - Jurnal Altifani Penelitian dan Pengabdian kepada Masyarakat
Publisher : Indonesian Scientific Journal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59395/altifani.v5i5.750

Abstract

Perkembangan ekonomi digital menuntut penguasaan keterampilan desain grafis sebagai bagian dari strategi komunikasi visual. Kegiatan pengabdian ini bertujuan untuk meningkatkan keterampilan desain grafis dasar siswa PKBM Bangkit di Kota Semarang guna mendukung kemandirian ekonomi kreatif. Metode pelaksanaan meliputi identifikasi kebutuhan, perencanaan berbasis praktik, pelatihan penggunaan aplikasi Canva, dan evaluasi partisipatif. Pelatihan dilakukan secara interaktif dan aplikatif, dengan materi yang mudah diakses dan disesuaikan dengan konteks kehidupan peserta. Hasil menunjukkan bahwa sebagian besar peserta merasa Canva mudah digunakan, dan memahami materi dengan cukup jelas. Peserta mampu menghasilkan desain sederhana untuk kebutuhan pribadi dan komunitas. Kegiatan ini membuktikan bahwa PKBM memiliki potensi sebagai wadah pengembangan keterampilan digital yang inklusif. Temuan ini memperkuat pentingnya integrasi pelatihan kreatif dalam pendidikan nonformal untuk mendukung pemberdayaan ekonomi masyarakat secara berkelanjutan.
SIDEMA (Sistem Informasi Desa Menawan) untuk Peningkatan Pelayanan Masyarakat dalam Upaya Mewujudkan Digital Village di Desa Menawan Kabupaten Grobogan Fikriah, Fari Katul; Nugaraha, Rozaq Isnaini; Verawati, Liesta
Jurnal Pengabdian UNDIKMA Vol. 5 No. 4 (2024): November
Publisher : LPPM Universitas Pendidikan Mandalika (UNDIKMA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33394/jpu.v5i4.13302

Abstract

This community service activity aims to implement the Menawan Village Information System (SIDEMA) to facilitate data management and services to the community and the dissemination of village information digitally. The implementation of this program includes an initial survey of technology needs, system implementation, implementation of socialization and training for residents and the Menawan Village apparatus, Grobogan Regency. The results of the community service show that the implementation of SIDEMA is able to increase service efficiency and facilitate residents' access to public information. SIDEMA is expected to function optimally in supporting the Digital Village vision. The evaluation carried out showed that the community was satisfied with the community service activities that had been carried out. Further development and replication of this system to other villages is a strategic step in realizing technology-based villages in Indonesia.
CLASSIFICATION OF DENGUE FEVER DISEASE USING A MACHINE LEARNING-BASED RANDOM FOREST ALGORITHM SETYAWAN, ARIF FITRA; Ariyanto, Amelia Devi Putri; Fikriah, Fari Katul
JIKO (Jurnal Informatika dan Komputer) Vol 7, No 2 (2024)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v7i2.8496

Abstract

Dengue Hemorrhagic Fever (DHF) is a tropical disease that often results in high morbidity and mortality rates. Early diagnosis of DHF is crucial to mitigate its adverse effects. However, manual diagnostic processes are often inefficient and prone to errors. This study aims to develop a DHF classification model using the Random Forest algorithm, which is expected to assist in the early diagnosis of this disease. The methodology used in this research is CRISP-DM (Cross-Industry Standard Process for Data Mining), which includes the stages of Business Understanding, Data Understanding, Data Preparation, Modeling, Evaluation, and Deployment. Data was obtained from kaggle.com, and during the Data Preparation stage, missing values were removed, categorical features were encoded, data was normalized, and split into training and testing sets. The research results show that the Random Forest model has an accuracy of 88.5%, precision of 88.2%, recall of 65.2%, F1-score of 74.9%, and ROC AUC of 0.810. Feature importance analysis revealed that the Gender_Male and Body_Pain features have the largest contributions in DHF classification. Although the model demonstrated high accuracy and precision, the lower recall value indicates that some positive cases were missed, requiring further improvements. The Random Forest can be used as a tool for early DHF diagnosis, but further adjustments are necessary to enhance its performance. This research provides insights into the contributing factors for DHF diagnosis and the practical application potential of this model in medical decision support systems.
Instance Selection dengan Naïve Bayes pada Klasifikasi Kanker Serviks Fikriah, Fari Katul
Jurnal Komtika (Komputasi dan Informatika) Vol 5 No 2 (2021)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v5i2.6041

Abstract

There are several deadly disease for woman, one of which is servical cancer. The growth and development of the disease is very slow, so that treatment if know form the beginning will facilitate the healing process, but conversely unknown cancers from the beginning will become dangereous and deadly disease due to relatively difficult healing. Biopsy action is one way to detect the presence of cancer. In the previous study, classification of cervical cancer had the bighest accuracy value of 97,515% using the decision tree method of several feature selection technique. for this reason, this research will use the decision tree or tree C4.5 classification method, logistic function and zeroR which were previously carried out processing with instance selection with Naïve Bayes by reducing the elimination of missing values with the aim of increasing the level of accuracy better than previous studies. C4.5 classification in this study has the most maximum results compared to other classification methods with an accuracy value of 99,69%.
Naïve Bayes untuk Klasifikasi Penyakit Daun Bawang Merah Berdasarkan Ekstraksi Fitur Gray Level Cooccurrence Matrix (GLCM) Fikriah, Fari Katul; Burhanis Sulthan, M; Mujahidah, Nailatul; Khoirur Roziqin, Moh.
Jurnal Komtika (Komputasi dan Informatika) Vol 6 No 2 (2022)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v6i2.7925

Abstract

Bawang merah merupakan salah satu produk pertanian yang menjadi bagian komoditas rempah-rempah yang biasa digunakan sebagai bahan masakan. Dalam pengelolaan bawang merah ini tentu terdapat beberapa kendala, pengamatan pada kondisi penanaman bawang merah bisa dilihat dari perubahan yang terjadi pada daunnya. Terdapat beberapa sebab yang menjadikan panen bawang merah menjadi menurun walaupun semakin banyak petani yang menanam bawang merah tersebut. Diantara penyebab gagalnya panen yang dialami petani bawang merah adalah adanya penyakit yang menyerang pada daunnya. Penyakit pada daun bawang merah juga beraneka macam serta memiliki beberapa gejala yang berbeda-beda. Klasifikasi pada penyakit daun bawang merah memberikan langkah untuk ketahananan tanaman yang berkelanjutan. Penyakit daun bawang merah harus diklasifikasikan berdasarkan jenisnya agar bisa mendapatkan penanganan yang tepat. Penelitian ini bertujuan untuk mengklasifikasikan penyakit daun bawang merah berdasarkan ekstraksi fitur Gray Level Co-occurrence Matrix (GLCM) yang didapat dari citra daun bawang merah dengan mengambil empat fitur yaitu energy, contrast, correlation serta homogeneity, sedangkan metode algoritma yang dipakai untuk klasifikasi penyakit daun bawang tersebut adalah Naïve Bayes dengan akurasi sebesar 62%.
Monitoring dan Klasifikasi Kualitas Air Kolam Ikan Gurami Berbasis Internet of Things Menggunakan Metode Naive Bayes Kristiyanto, Arip; Fikriah, Fari Katul; Inkiriwang, Rully; Andriansah, Zulfi
Jurnal Komtika (Komputasi dan Informatika) Vol 7 No 2 (2023)
Publisher : Universitas Muhammadiyah Magelang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31603/komtika.v7i2.10200

Abstract

Ministry of Marine Affairs and Fisheries (KKP) noted that Indonesia produced 56,539 tons of gourami fish in the second quarter of 2022 High market demand and economical selling prices encourage farmers to cultivate gourami fish. In cultivating gourami fish there are several obstacles, for example, disease caused by poor water quality. Water quality is the main parameter in the success of gourami fish farming. This research aims to develop a water quality monitoring system based on the Internet of Things. The system prototype uses a temperature sensor (DS18B20), Ph sensor (dfrobot SEN0161), turbidity sensor (dfrobot SEN0189), flowmeter, and ultrasonic sensor (JSN-SR04) as input. The Arduino Mega R3 microcontroller is the processor and the Oled module (SSD1306) is the output. Thingboard is a cloud server that functions as sensor data monitoring. Temperature sensor testing results (DS18B20) average error 0.48%, Ph(dfrobot SEN0161) sensor testing average error 0.64%, ultrasonic sensor testing (JSN-SR04) average error 7.83%, testing Turbidity sensors can measure the level of water turbidity. Next, the water quality parameter data is processed using the Naïve Bayes algorithm method for classifying the water quality of gourami ponds. The results of this classification obtained an accuracy of 99.94% a Kappa Statistics value of 0.9989 and a Mean Absolute Error of 0.0003
Co-Authors Aditya Putra Ramdani Amelia Devi Putri Ariyanto Amelia Devi Putri Ariyanto Andriansah, Zulfi Arif Fitra Setyawan Arif Fitra Setyawan Arip Kristiyanto Ariyanto , Amelia Devi Putri Basirudin Ansor Burhanis Sulthan, M Devi Putri Ariyanto, Amelia Fitra Setiawan, Arif Husni Hidayat Husni Hidayat Inkiriwang, Rully Khoirur Roziqin, Moh. M Burhanis Sulthan M. Rudi Fanani Moh. Khoirur Roziqin Muhamad Rizaludin Mujahidah, Nailatul Mutiarachim, Atika Nailatul Mujahidah Nugaraha, Rozaq Isnaini Nur Hadian Rizal, Muhamad Rizaludin Rozaq Isnaini Nugraha Setyawan, Arif Fitra Verawati, Liesta