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All Journal ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Inspiratif Pendidikan Jurnal Teknologi Informasi dan Ilmu Komputer Journal of Information Systems Engineering and Business Intelligence KLIK (Kumpulan jurnaL Ilmu Komputer) (e-Journal) Sistemasi: Jurnal Sistem Informasi Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control UICELL Conference Proceeding Jurnal Sains dan Informatika JURNAL ILMIAH INFORMATIKA Hearty : Jurnal Kesehatan Masyarakat Jurnal Biomedika dan Kesehatan Psikologi Konseling: Jurnal Kajian Psikologi dan Konseling Journal of Electronics, Electromedical Engineering, and Medical Informatics Jurnal Pengabdian Kepada Masyarakat (Mediteg) Health Information : Jurnal Penelitian Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences JOURNAL LA MEDIHEALTICO MAHESA : Malahayati Health Student Journal Fitrah: Journal of Islamic Education Multidiciplinary Output Research for Actual and International Issue (Morfai Journal) Jurnal Kolaboratif Sains Prosiding Seminar Nasional Sisfotek (Sistem Informasi dan Teknologi Informasi) Journal of Data Science and Software Engineering Jurnal INFOTEL Jurnal Pengabdian Kepada Masyarakat Itekes Bali JUKEJ: Jurnal Kesehatan Jompa Jurnal Informatika Polinema (JIP) Jurnal Kesehatan Masyarakat Perkotaan Indonesian Journal of Electronics, Electromedical Engineering, and Medical Informatics Holistik Jurnal Kesehatan
Fatma Indriani
Program Studi Ilmu Komputer, Fakultas Matematika Dan Pengetahuan Alam, Universitas Lambung Mangkurat, Indonesia
Religion Agriculture, Biological Sciences & Forestry Arts Humanities Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Health Professions Immunology & microbiology Languange, Linguistic, Communication & Media Law, Crime, Criminology & Criminal Justice Library & Information Science Materials Science & Nanotechnology Mathematics Medicine & Pharmacology Nursing Public Health Social Sciences Veterinary Other

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HUBUNGAN LITERASI KESEHATAN MENTAL DENGAN SIKAP MENCARI BANTUAN PROFESIONAL PSIKOLOGI PADA MAHASISWA FKM UIN SUMATERA UTARA Hayati, Sera Br; Indriani, Fatma
PSIKOLOGI KONSELING Vol 15, No 2 (2023): Jurnal Psikologi Konseling
Publisher : Universitas Negeri Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24114/konseling.v15i2.55278

Abstract

Seseorang dapat menyadari potensi sebenarnya dari dirinya, mengatasi stres, bekerja dengan hasil berdaya guna, dan berkontribusi terhadap lingkungannya ketika berada dalam kondisi yang baik. Penelitian ini bertujuan untuk mengetahui hubungan literasi kesehatan mental dengan kecenderungan mahasiswa FKM UIN Sumatera Utara mencari bantuan profesional psikologi. Penelitian ini menggunakan metodologi kuantitatif dengan menggunakan metodologi cross sectional. Sebanyak 154 responden dijadikan sampel. Pengumpulan data dilakukan melalui penggunaan kuesioner dan analisis data yang dikumpulkan dianalisis menggunakan uji univariat dan bivariat. Literasi kesehatan mental mahasiswa FKM UIN Sumatera Utara berada pada kategori tinggi sebanyak 117 responden atau 76,0%. Sikap mencari bantuan professional mahasiswa FKM UIN Sumatera Utara berada pada kategori baik yaitu sebanyak 95 responden atau 61,7%. Hasil penelitian bivariat pada penelitian ini menunjukkan angka signifikansi (α) yakni 0,516. Hal ini menunjukkan bahwa penelitian ini tidak menemukan adanya korelasi antara literasi kesehatan mental dengan sikap mencari bantuan profesional pada mahasiswa FKM UIN Sumatera Utara.Kata Kunci: Literasi Kesehatan Mental, Sikap Mencari Bantuan Profesional
Analisis Determinan Perilaku Pemeriksaan Payudara Sendiri (Sadari) Pada Remaja Putri carolina, ayu; Indriani, Fatma; Ismah, Zata
Health Information : Jurnal Penelitian Vol 16 No 2 (2024): Mei-Agustus
Publisher : Poltekkes Kemenkes Kendari

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.36990/hijp.v16i2.1511

Abstract

Kanker payudara menjadi kanker terbanyak yang diderita oleh wanita, Riset Penyakit Tidak Menular mengatakan jika perilaku masyarakat dalam deteksi dini kanker payudara masih rendah. Penelitian ini bertujuan untuk mengetahui determinan yang berhubungan dengan perilaku SADARI pada siswi di SMAN 1 Kutalimbaru. Metode yang digunakan yakni kuantitatif dengan desain penelitian cross-sectional. Sampel penelitian diambil menggunakan metode stratified random sampling hingga diperoleh sebanyak 88 sampel. Sumber data penelitian menggunakan data primer yang diperoleh secara langsung melalui pengisian kuesioner. Data di analisis secara univariat dan bivariat. Hasil penelitian ini diperoleh nilai signifikan sebesar <0,05 sehingga adanya hubungan. Besarnya hubungan dilihat dari nilai koefisien korelasi untuk pengetahuan sebesar 0,290, untuk ketepaparan informasi sebesar sebesar 0,382 dan dukungan keluarga sebesar 0,465. Nilai koefisien korelasi yang bernilai positif ini menunjukkan adanya hubungan yang searah semakin tinggi pengetahuan, ketepaparan informasi serta dukungan keluarga yang dimiliki remaja maka akan semakin baik perilaku SADARI. Determinan yang berhubungan dengan perilaku SADARI remaja putri yaitu pengetahuan, ketepaparan informasi, dan dukungan keluarga. Saran yang dapat diberikan yakni dengan memberikan edukasi terhadap para ibu tentang perilaku SADARI sehingga dengan meningkatnya pengetahuan ibu ini diharapkan seorang ibu dapat memberikan dukungan kepada putrinya yang mana hal ini akan sejalan dengan meningkatnya perilaku SADARI.
THE FOREIGN LANGUAGE LEARNING ANXIETY: THE DESCRIPTIVE OF COMMUNICATION APPREHENSION, TEST ANXIETY AND FEAR OF NEGATIVE EVALUATION Nurhayani, Nurhayani; Indriani, Fatma; Hasyimi , Ali
UICELL No 7 (2023): UICELL Conference Proceedings 2023 (in progress)
Publisher : Universitas Muhammadiyah Prof. DR. HAMKA

Show Abstract | Download Original | Original Source | Check in Google Scholar

Abstract

The study sought to investigate the description of learning anxiety of foreign language in three component : fear of communication apprehension, test anxiety and fear of negative evaluation. Subjects of this research were 40 students of english department and 47 students of arabic department. The learning anxiety scale is used to collect the data. It is adapted based on learning anxiety scale of FLCAS (Foreign Language Classroom Anxiety Scale) developed by Horwitz et al in 1986, consisting of 33 items. Analysis of descriptive by using SPSS 25 as applied to analyze the data. The result of the analysis shows that foreign language learning anxiety among English department students are 24.4 % high, 73,2% medium and 2,4% low. The foreign language learning anxiety among Arabic department students are 17 high, 78,7 % medium and 4,3% low. The research found the anxiety of communication apprehension among students of English department are 36.6% high, 61% medium and 2.4% low. The anxiety of communication apprehension among students of Arabic department are 21.3% high, 76.6 % medium and 2.1 % low. The aspect of test anxiety among students of English department are 4.9% high, 75.6 medium and 19.5 low. The result of anxiety among students of Arabic department are are 10.6% high, 85.1% medium and 4.3% low. The aspect of Fear of negative evaluation among university students of English department are 39% high, 58.5% medium and 2.4% low. The aspect of Fear of negative evaluation among students of Arabic department are 31.9% high, 63.8 % medium and 4.3% low. Keywords: learning anxiety, foreign language learning, communication apprehension, test anxiety, negative evaluation
Image Classification of Traditional Indonesian Cakes Using Convolutional Neural Network (CNN) Azizah, Azkiya Nur; Budiman, Irwan; Indriani, Fatma; Faisal, Mohammad Reza; Herteno, Rudy
Computer Engineering and Applications Journal Vol 13 No 2 (2024)
Publisher : Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/comengapp.v13i2.469

Abstract

Indonesia is one of the countries famous for its traditional culinary. Traditional cakes in Indonesia are traditional snacks typical of the archipelago's culture which have a variety of textures, shapes, colors that vary and some are similar so that there are still many people who do not know the name of the cake from the many types of traditional Indonesian cakes. The problem can be solved by creating a traditional cake image recognition system that can be programmed and trained to classify various types of traditional Indonesian cakes. The Convolutional Neural Network method with the AlexNet architecture model is used in this research to predict various kinds of traditional Indonesian cakes. The dataset used in this research is 1846 datasets with 8 classes of cake images. This study trained the AlexNet model with several optimizers, namely, Adam optimizer, SGD, and RMSprop. The best parameters from the model testing results are at batchsize 16, epoch 50, learning rate 0.01 for SGD optimizer and learning rate 0.001 for Adam and RMSprop optimizers. Each optimizer tested produces different accuracy, precision, recall, and f1_score values. The highest test results that have been carried out on the image dataset of typical Indonesian traditional cakes are obtained by the Adam optimizer with an accuracy value of 79%.
LSTM and Bi-LSTM Models For Identifying Natural Disasters Reports From Social Media Yunida, Rahmi; Faisal, Mohammad Reza; Muliadi; Indriani, Fatma; Abadi, Friska; Budiman, Irwan; Prastya, Septyan Eka
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.319

Abstract

Natural disaster events are occurrences that cause significant losses, primarily resulting in environmental and property damage and in the worst cases, even loss of life. In some cases of natural disasters, social media has been utilized as the fastest information bridge to inform many people, especially through platforms like Twitter. To provide accurate categorization of information, the field of text mining can be leveraged. This study implements a combination of the word2vec and LSTM methods and the combination of word2vec and Bi-LSTM to determine which method is the most accurate for use in the case study of news related to disaster events. The utility of word2vec lies in its feature extraction method, transforming textual data into vector form for processing in the classification stage. On the other hand, the LSTM and Bi-LSTM methods are used as classification techniques to categorize the vectorized data resulting from the extraction process. The experimental results show an accuracy of 70.67% for the combination of word2vec and LSTM and an accuracy of 72.17% for the combination of word2vec and Bi-LSTM. This indicates an improvement of 1.5% achieved by combining the word2vec and Bi-LSTM methods. This research is significant in identifying the comparative performance of each combination method, word2vec + LSTM and word2vec + Bi-LSTM, to determine the best-performing combination in the process of classifying data related to earthquake natural disasters. The study also offers insights into various parameters present in the word2vec, LSTM, and Bi-LSTM methods that researchers can determine.
Implementation of Random Forest and Extreme Gradient Boosting in the Classification of Heart Disease using Particle Swarm Optimization Feature Selection Ansyari, Muhammad Ridho; Mazdadi, Muhammad Itqan; Indriani, Fatma; Kartini, Dwi; Saragih, Triando Hamonangan
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 5 No 4 (2023): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v5i4.322

Abstract

Heart disease is a condition that ranks as the primary cause of death worldwide. Based on available data, over 36 million people have succumbed to non-communicable diseases, and heart disease falls within the category of non-communicable diseases. This research employs a heart disease dataset from the UCI Repository, consisting of 303 instances and 14 categorical features. In this research, the data were analyzed using the classification methods XGBoost (Extreme Gradient Boosting) and Random Forest, which can be applied with PSO (Particle Swarm Optimization) as a feature selection technique to address the issue of irrelevant features. This issue can impact prediction performance on the heart disease dataset. From the results of the conducted research, the obtained values for the XGBoost (Extreme Gradient Boosting) model were 0.877, and for the Random Forest model, it was 0.874. On the other hand, in the model utilizing Particle Swarm Optimization (PSO), the obtained AUC values are 0.913 for XGBoost (Extreme Gradient Boosting) and 0.918 for Random Forest. These research results demonstrate that PSO (Particle Swarm Optimization) can enhance the AUC of heart disease prediction performance. Therefore, this research contributes to enhancing the precision and efficiency of heart disease patient data processing, which benefits heart disease diagnosis in terms of speed and accuracy.
Sentiment Analysis of TikTok Shop Closure in Indonesia on Twitter Using Supervised Machine Learning Al Habesyah, Noor Zalekha; Herteno, Rudy; Indriani, Fatma; Budiman, Irwan; Kartini, Dwi
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.381

Abstract

TikTok Shop is one of the features in TikTok application which facilitates users to buy and sell products. The integration of TikTok Shop with social media has provided new opportunities to reach customers and increase sales. However, the closure of TikTok Shop has caused controversy among the public. This study aims to analyze the views and responses of TikTok users in Indonesia to the closure of TikTok Shop. The dataset used was obtained from Twitter. The research methodology consists of labeling, oversampling, splitting, and machine learning, which includes SVM, Random Forest, Decision Tree, and Deep Learning (H2O). The contribution of this research enriches our understanding of the implementation of machine learning, especially in sentiment analysis of TikTok Shop closures. From the test results, it is known that Deep Learning (H2O) + SMOTE obtained AUC 0.900, without using SMOTE, AUC 0.867. SVM + SMOTE obtained AUC 0.885, without using SMOTE AUC 0.881. Random Forest + SMOTE obtained AUC 0.822, while without using SMOTE AUC 0.830. Decision Tree + SMOTE AUC 0.59; without SMOTE, AUC 0.646. Deep Learning (H2O) with SMOTE produces better performance compared to SVM, Random Forest, and Decision Tree. With an AUC of 0.900; it can be said that Deep Learning (H2O) has excellent performance for sentiment analysis of TikTok Shop closures. This research has significant implications for social electronic commerce due to its potential utilization by social media analysts.
Implementation of C5.0 Algorithm using Chi-Square Feature Selection for Early Detection of Hepatitis C Disease MAHMUD, Mahmud; BUDİMAN, Irwan; INDRİANİ, Fatma; KARTİNİ, Dwi; FAİSAL, Mohammad Reza; ROZAQ, Hasri Akbar Awal; YILDIZ, Oktay; Caesarendra, Wahyu
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 2 (2024): April
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i2.384

Abstract

Hepatitis C, a significant global health challenge, affects 71 million people worldwide, with severe complications such as cirrhosis and hepatocellular carcinoma. Despite its prevalence and availability in rapid diagnostic tests (RDTs), the need for accurate early detection methods remains critical. This research aims to enhance hepatitis C virus classification accuracy by integrating the C5.0 algorithm with Chi-Square feature selection, addressing the limitations of current diagnostic approaches and potentially reducing diagnostic errors. This research explores the development of a machine learning model for hepatitis C prediction, utilizing a publicly available dataset from Kaggle. It encompasses preprocessing techniques such as label encoding, handling missing values, normalization, feature selection, model development, and evaluation to ensure the model's efficacy and accuracy in diagnosing hepatitis C. The findings of this study reveal that implementing Chi-Square feature selection significantly enhances the effectiveness of machine learning algorithms. Specifically, the combination of the C5.0 algorithm and Chi-Square feature selection yielded a remarkable accuracy of 96.75%, surpassing previous research benchmarks. This highlights the potent synergy between advanced feature selection techniques and machine learning algorithms in improving diagnostic precision. The study conclusively demonstrates that machine learning is an effective tool for detecting hepatitis C, showcasing the potential to enhance diagnostic accuracy significantly. As a future recommendation, adopting AutoML is suggested to periodically automate the selection of the optimal algorithm, promising further improvements in detection capabilities.
Gender Classification on Social Media Messages Using fastText Feature Extraction and Long Short-Term Memory Sa’diah, Halimatus; Faisal, Mohammad Reza; Farmadi, Andi; Abadi, Friska; Indriani, Fatma; Alkaff, Muhammad; Abdullayev, Vugar
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 3 (2024): July
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i3.407

Abstract

Currently, social media is used as a platform for interacting with many people and has also become a source of information for social media researchers or analysts. Twitter is one of the platforms commonly used for research purposes, especially for data from tweets written by individuals. However, on Twitter, user information such as gender is not explicitly displayed in the account profile, yet there is a plethora of unstructured information containing such data, often unnoticed. This research aims to classify gender based on tweet data and account description data and determine the accuracy of gender classification using machine learning methods. The method used involves FastText as a feature extraction method and LSTM as a classification method based on the extracted data, while to achieve the most accurate results, classification is performed on tweet data, account description data, and a combination of both. This research shows that LSTM classification on account description data and combined data obtained an accuracy of 70%, while tweet data classification achieved 69%. This research concludes that FastText feature extraction with LSTM classification can be implemented for gender classification. However, there is no significant difference in accuracy results for each dataset. However, this research demonstrates that both methods can work well together and yield optimal results.
Application Of SMOTE To Address Class Imbalance In Diabetes Disease Classification Utilizing C5.0, Random Forest, And SVM M. Khairul Rezki; Mazdadi, Muhammad Itqan; Indriani, Fatma; Muliadi, Muliadi; Saragih, Triando Hamonangan; Athavale, Vijay Annant
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 6 No 4 (2024): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v6i4.434

Abstract

The implementation of SMOTE to tackle class imbalance in classification frequently results in suboptimal outcomes, owing to the intricacy of the dataset and the multitude of attributes at play. Consequently, alternative classification models were explored through experimentation to gauge their precision. This research aims to compare the precision of C5.0, Random Forest, and SVM classification models both with and without SMOTE. The methodology encompasses dataset selection, an overview of classification algorithms (C5.0, Random Forest, SVM), SMOTE technique, validation via split validation, preprocessing involving min-max normalization, and execution evaluation utilizing confusion matrices and AUC analysis. The dataset was sourced by Kaggle, specifically to rectify class imbalance in a diabetes dataset using SMOTE, consisting of 768 instances, with 268 samples for diabetic cases and 500 samples for non-diabetic cases. Prior to SMOTE application, the classification precision for C5.0, Random Forest, and SVM were 0.714, 0.733, and 0.746 respectively, with corresponding AUC values of 0.745, 0.824, and 0.799. Post-SMOTE, the precision depicts for the same techniques were 0.603, 0.727, and 0.727, with AUC values of 0.734, 0.831, and 0.794 respectively. It can be inferred that there's minimal impact post-SMOTE across the three classification models due to potential overfitting on the dataset, leading to excessive reliance on synthesized data for minority classes, resulting in diminished model execution, precision, and AUC scores.
Co-Authors Abdilah, Muhammad Fariz Fata Abdul Azis Abdullayev, Vugar Achmad Rizal Afifa, Ridha Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Ahmad Rusadi Arrahimi - Universitas Lambung Mangkurat) Al Habesyah, Noor Zalekha Amini, Aisah Ananda, Zahra Andi Farmadi Andi Farmadi Anshari, Muhammad Ridha Ansyari, Muhammad Ridho Arianti, Tiara Aryanti, Agustia Kuspita Asti, Rahmah Dwi Astuti, Yeni Ayu Astuty, Delfriana Ayu Athavale, Vijay Annant Azizah, Azkiya Nur Badali, Rahmat Amin Baharuddin Siregar, Baharuddin Baron Hidayat Barus, Nency Utami Br Berutu, Marwiyah Br Barus, Nency Utami br Damanik, Cici Rahayu Carolina, Ayu DALIMUNTHE, NADIYAH RAHMA Darmansyah, Rendi Dendy Fadhel Adhipratama Dendy Dewi Sri Wahyuni, Dewi Sri Difa Fitria Dodon Turianto Nugrahadi Dwi Kartini Dwi Kartini, Dwi Effendi, Khairunnisa Fahmi Setiawan Fairudz Shahura Faisal, M. Reza Faisal, Mohammad Reza Fajrin Azwary Fitriani, Karlina Elreine Friska Abadi Ghinaya, Helma Gustara, Rizki Asih Hafizah, Rini Harahap, Helma Denisah Hartati Hartati Hasyimi , Ali Hayati, Sera Br Hermiati, Arya Syifa Herteno, Rudi Heru Kartika Chandra I Gusti Ngurah Antaryama Ichwan Dwi Nugraha Ihsan, Muhammad Khairi Irwan Budiman Irwan Budiman Khairiyah Dwie Vanesa Lilies Handayani Lubis, Masruroh M. Apriannur M. Khairul Rezki Mahmud Mahmud Mawandri, Dwi Mohammad Mahfuzh Shiddiq Muhammad Alkaff Muhammad Itqan Mazdadi Muhammad Nadim Mubaarok Muhammad Reza Faisal, Muhammad Reza Muhammad Ridha Maulidi Muliadi Muliadi Muliadi Aziz Nafiz, Muhammad Fauzan Nita Arianty Nofi Susanti Nurhayani nurhayani Nurhayati Octavia, Mayang Dwi Oni Soesanto P., Chandrasekaran Patrick Ringkuangan Prastya, Septyan Eka Purnajaya, Akhmad Rezki Putri Maimunah Radityo Adi Nugroho Rapotan Hasibuan Reni Agustina Harahap Riadi, Agus Teguh Risma, Ade Ritonga, Egril Rehulina Rozaq, Hasri Akbar Awal Rudy Herteno Salianto Salianto, Salianto Saputro, Setyo Wahyu Saragih, Triando Hamonangan Sa’diah, Halimatus Selvia Indah Liany Abdie Siregar, Nurul Syahputri Soesanto, Oni Sri Rahayu Suci Wulandari Triyoolanda, Anggun Utami, Tri Niswati Wahyu Caesarendra Wardana, Muhammad Difha Wati, Desi Indriani Rahma Wijaya Kusuma, Arizha YILDIZ, Oktay Yulia Khairina Ashar Yunida, Rahmi Zahra, Fairuz Zakwan, M. Hadin Zali, Muhammad Zata Ismah Zida Ziyan Azkiya