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All Journal Jurnal Teknologi dan Manajemen Informatika Jurnal Teknologi Informasi dan Ilmu Komputer International Journal of Advances in Intelligent Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) ILKOM Jurnal Ilmiah EDUMATIC: Jurnal Pendidikan Informatika Journal of Intelligent Decision Support System (IDSS) Computer Science and Information Technologies Indonesian Journal of Data and Science Idealis : Indonesia Journal Information System Jurnal Teknik Informatika (JUTIF) Journal of Applied Data Sciences Buletin Sistem Informasi dan Teknologi Islam Jurnal Informatika: Jurnal Pengembangan IT International Journal of Artificial Intelligence in Medical Issues The Indonesian Journal of Computer Science Linier: Literatur Informatika dan Komputer
Darwis, Herdianti
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Dentistry Education Electrical & Electronics Engineering Engineering Health Professions Mathematics Medicine & Pharmacology Public Health

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An In-depth Exploration of Sentiment Analysis on Hasanuddin Airport using Machine Learning Approaches Lilis Nur Hayati; Fitrah Yusti Randana; Darwis, Herdianti
Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Vol 9 No 2 (2025): April 2025
Publisher : Ikatan Ahli Informatika Indonesia (IAII)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29207/resti.v9i2.6253

Abstract

Machine learning-based sentiment analysis has become essential for understanding public perceptions of public services, including air transportation. Sultan Hasanuddin Airport, one of the main gateways in eastern Indonesia, faces the challenge of improving services amid changing user needs due to the COVID-19 pandemic. This study aims to compare the effectiveness of three machine learning algorithms- Support Vector Machine (SVM), Naive Bayes Multinomial, and K-Nearest Neighbor (KNN)-in analyzing the sentiment of user reviews related to airport services. The research also explores data splitting techniques, text preprocessing, data balancing using SMOTE, model validation, and method parameterization to ensure optimal results. The review data was retrieved from Google Maps (2021-2024) and underwent manual labelling. Text preprocessing includes normalization, stemming using Sastrawi, and stopword removal. The data-balancing technique uses SMOTE, while model evaluation is done with stratified k-fold cross-validation. SVM with a linear kernel showed the best performance, achieving an F1-score of 98.4%. Naive Bayes performed optimally, achieving an F1-score of 93.9%, while KNN recorded the best F1-score of 92.0%. SMOTE was shown to improve Naive Bayes' performance on unbalanced datasets, although it did not significantly impact SVM. The findings of this study provide data-driven recommendations to improve services at Sultan Hasanuddin Airport, such as the management of cleaning facilities, waiting room comfort, and passenger flow efficiency. In addition, this research opens up opportunities for developing real-time sentiment analysis systems that can be applied in other air transportation sectors.
A Comparative Study of Public Opinion on Indonesian Police: Examining Cases in the Aftermath of the Kanjuruhan Football Disaster Purnawansyah, Purnawansyah; Raja, Roesman Ridwan; Darwis, Herdianti
Indonesian Journal of Data and Science Vol. 6 No. 2 (2025): Indonesian Journal of Data and Science
Publisher : yocto brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijodas.v6i2.235

Abstract

This research explores public sentiment towards the Indonesian police using sentiment analysis and machine learning techniques. The study addresses the challenge of understanding public opinion based on social media comments related to significant police cases. The aim is to compare reported satisfaction levels with actual public sentiment. Utilizing the Indonesian RoBERTa base IndoLEM sentiment classifier, comments were analyzed and preprocessed. The classification was conducted using Random Forest (RF) and Complement Naive Bayes (CNB) models, incorporating unigram and bi-gram features. Oversampling techniques were applied to handle data imbalance. The best-performing model, Random Forest with bi-gram features, achieved high evaluation scores, including a precision of 0.91 and accuracy of 0.91. The findings reveal significant insights into public opinion, contributing to improved law enforcement strategies and public trust.
Fourier Descriptor Pada Klasifikasi Daun Herbal Menggunakan Support Vector Machine Dan Naive Bayes Samir, Mutmainnah; Purnawansyah; Darwis, Herdianti; Umar, Fitriyani
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 6: Desember 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2023107309

Abstract

Daun herbal bermanfaat sebagai obat alternatif karena kandungan alaminya dapat menyembuhkan berbagai penyakit dan menjaga kesehatan tubuh. Klasifikasi citra daun herbal digunakan untuk membedakan jenis tanaman herbal berdasarkan bentuk daun. Penelitian ini Penelitian menggunakan Fourier Descriptor (FD) untuk mengekstraksi fitur pada daun herbal dan mengklasifikasikannya menggunakan metode Support Vector Machine (SVM) dan Naive Bayes (NB). SVM diimplementasikan dengan empat kernel yaitu Linear, polynomial, Radial Basis Function (RBF), dan sigmoid sementara Naive bayes diaplikasikan dengan tiga jenis kernel yaitu Gaussian, Multinomial, Bernoulli. Evaluasi kinerja menggunakan Precision, accuracy F1-Score dan Recall. Citra daun herbal terdiri dari daun katuk (Sauropus Androgynus) dan daun kelor (Moringa) dengan total 480 citra. Data tersebut dibagi menjadi 80% untuk training dan 20% untuk testing. Terdapat dua skenario pencahayaan yaitu kondisi gelap dan terang. Hasil penelitian menunjukkan bahwa perbandingan metode SVM dengan ekstraksi FD dimana kernel Linear mencapai akurasi sebesar 98% pada skenario gelap, sementara kernel Sigmoid memberikan akurasi terendah sebesar 44% pada scenario gelap maupun terang. Adapun hasil dari metode Naive bayes dengan ekstraksi FD pada kernel multinomial menghasilkan akurasi tertinggi sebesar 83% pada terang, sedangkan kernel Bernoulli memberikan akurasi terendah sebesar 46% pada skenario gelap dan terang. Berdasarkan perbandingan hasil klasifikasi dari kedua metode, disarankan bahwa metode SVM pada ekstraksi FD lebih direkomendasikan dalam proses klasifikasi daun herbal. Penelitian ini dapat memberikan rekomendasu pengembang sistem untuk menetapkan metode yang tepat dalam klasifikasi citra daun herbal.   Abstract Herbal leaves are beneficial as alternative medicine because their natural content can cure various diseases and maintain a healthy body. The classification of herbal leaf images is used to differentiate types of herbal plants based on leaf shapes. This study utilizes Fourier Descriptor (FD) to extract features from herbal leaves and classify them using the Support Vector Machine (SVM) and Naive Bayes (NB) methods. SVM is implemented with four kernels namely linear, polynomial, Radial Basis Function (RBF), and Sigmoid while Naive bayes is applied with three types of kernels namely Gaussian, multinomial, Bernoulli. Performance evaluation includes precision, accuracy, F1- score and recall. Herbal leaf images consist of leaves (Sauropus Androgynus) and moringa leaves with a total of 480 images. The data is divided into 80% for training and 20 % for testing. There are two lighting scenarios, namely dark and light conditions. The result of this study shows a comparison of the SVM method with FD extraction where the Linear kernel achieves the highest accuracy of 98% in dark scenarios, while the Sigmoid kernel provides the lowest accuracy of 44% in both dark and light scenarios. The result of the naïve bayes method with FD extraction on the Multinomial kernel yield the highest accuracy of 83% in light scenarios while the Bernoulli kernel provides the lowest accuracy 46% in both dark and light scenarios. Based on the comparison of the classification result of the two methods, it is suggested that the SVM method for FD extraction is more recommended in the herbal leaf classification process. This research can provide recommendation for system developers to determine the appropriate method for classifying herbal leaf images.  
DIGITAL IMAGE CLASSIFICATION OF HERBAL LEAVES USING KNN AND CNN WITH GLCM FEATURES Zahirah, Dinna; Purnawansyah, Purnawansyah; Kurniati, Nia; Darwis, Herdianti
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 1 (2024): JUTIF Volume 5, Number 1, February 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.1.1162

Abstract

Geographical position and having a tropical climate make Indonesia known for its abundant biodiversity, one of which is herbal leaves. Indonesia has more than 2039 species that fall into the category of herbal medicinal plants. Herbal leaves are plants that are used as an alternative to natural disease healing. The large number of herbal leaf plants makes it difficult for people to distinguish between herbal plants and non-herbal plants, except when herbal leaf plants bear fruit or bloom. With advances in technology, many studies have been conducted to identify types of herbal plants, one of which is to identify the characteristics of the leaves. In this study, image recognition of herbal leaves was carried out using the K-Nearest Neighbor and Convolutional Neural Network methods with feature extraction of the Gray Level Co-occurance Matrix. By using these 2 methods, the data collected in this study were 480 leaf images which were then divided into 80% testing data and 20% training data. The data used are in the form of Sauropus androgynus and Moringa leaves. Based on the test results, the Convolutional Neural Network method which is suggested in the herbal leaf image classification which has an accuracy value of 96%..
Sistem Pakar Mendiagnosis Penyakit Gangguan Mental dengan Metode Certainty Factor Berbasis Android Darwis, Herdianti; Rahmasari, Putri Aulia; Irawati, Irawati
Buletin Sistem Informasi dan Teknologi Islam (BUSITI) Vol 6, No 2 (2025)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/busiti.v5i4.2391

Abstract

Sistem pakar yakni sebuah sistem yang diciptakan berdasarkan keahlian seorang pakar pada bidang terkhusus kedalam sebuah program komputer. Penelitian ini membahas tentang Sistem Pakar Mendiagnosis Penyakit Gangguan Mental Dengan  Metode Certainty Factor. Gangguan mental yakni sebuah keadaan kesehatan yang memengaruhi perasaan, pemikiran, perilaku, serta suasana hati atau gabungan diantaranya. Metode certainty factor dipakai sebagai nilai guna melakukan pengukuran taraf keyakinan penyakit gangguan mental. Penelitian ini mempunyai tujuan guna menghasilkan aplikasi yang bisa memberi bantuan masyarakat dalam melakukan diagnosa dini pada gejala awal penyakit gangguan mental. Pada pengujian akurasi yang dilakukan menghasilkan nilai akurasi pada sistem yaitu sebesar 80% berdasarkan 10 sampel. Aplikasi sistem pakar melakukan diagnosis penyakit gangguan mental telah berhasil diimplementasikan kedalam sistem memakai metode certainty factor guna mengambil kesimpulan berdasarkan pengetahuan pakar.
Hybrid Fourier Descriptor Naïve Bayes dan CNN pada Klasifikasi Daun Herbal Backar, Sunarti Passura; Purnawansyah, Purnawansyah; Darwis, Herdianti; Astuti, Wistiani
Jurnal Informatika: Jurnal Pengembangan IT Vol 8, No 2 (2023)
Publisher : Politeknik Harapan Bersama

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30591/jpit.v8i2.5186

Abstract

Plants are vital to human life on earth, and the leaves and their whole parts have many benefits. These parts of the plant can help distinguish between different species. The leaf identification can be performed at any time, while the other parts of the plants can only be identified at a certain time. The study aims to classify two types of herbs i.e. saur-opus androgynous and moringa oleifera, implementing the Fourier Descriptor method to extract the shape and texture features. In the process of classification using the Naïve Bayes method with three types of nuclei (Gaussian, Bernoulli, and Multinomial) and a Convolutional Neural Network. The testing process was carried out using two scenarios, dark and light, where each scenario consisted of 240 images for a total of 480 images divided into 20% of the data testing and 80% of the training data. The Fourier Descriptor-Bernoulli Naive Bayes method gives the lowest accuracy in both light and dark scenarios, at 46% and 52%, respectively. As for the classification of herbal leaves using a combination of the Fourier Descriptor-Convolutional Neural Network method, it is recommended to be used in light image scenarios and Fourier Descriptor-Gaussian Naive Bayes in the dark scenarios because it is able to detect herbal leaf types with 100% accuracy.
Comparative Machine Learning Models for Dementia Prediction Using SMOTE Puspitasari, Rahma; Amaliah, Tazkirah; Darwis, Herdianti
International Journal of Artificial Intelligence in Medical Issues Vol. 3 No. 2 (2025): International Journal of Artificial Intelligence in Medical Issues
Publisher : Yocto Brain

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56705/ijaimi.v3i2.351

Abstract

Dementia is a progressive neurodegenerative disorder that leads to cognitive decline and significantly affects patients' quality of life. Early detection is crucial for determining appropriate medical interventions and slowing disease progression. This study aims to develop a machine learning-based dementia prediction model and compare the performance of three algorithms: Support Vector Machine (SVM), Random Forest (RF), and XGBoost. The dataset, obtained from the Kaggle platform, consists of 373 MRI-based patient records categorized into three diagnosis groups: Converted, Demented, and Nondemented. To address class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied. Experimental results show that the XGBoost algorithm achieved the best performance, with an accuracy of 93.86%, precision of 94%, recall of 94%, and F1-score of 94%, outperforming SVM and Random Forest. The application of SMOTE improved the model’s sensitivity to minority classes. The combination of XGBoost and SMOTE demonstrates high accuracy in dementia prediction and holds potential for integration into clinical decision support systems (CDSS) to assist early diagnosis.
Analisis Sentimen Masyarakat Terhadap Sistem Pembayaran Mypertamina dengan Metode Random Forest, SVM, dan Naïve Bayes Amelia, Ayu; Hayati, Lilis Nur; Darwis, Herdianti
LINIER: Literatur Informatika dan Komputer Vol 1, No 1 (2024)
Publisher : Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/linier.v1i1.2269

Abstract

PT. Pertamina (PERSERO), sebagai perusahaan BUMN terkemuka di Indonesia di bidang perminyakan, memiliki peran vital dalam pengolahan dan pemasaran minyak bumi, terutama bahan bakar minyak (BBM). Penelitian ini menerapkan tiga metode analisis sentimen yaitu Random Forest, SVM, dan Naïve Bayes untuk mengevaluasi ulasan pengguna terhadap aplikasi MyPertamina. Dengan mengumpulkan data melalui web scraping dari Google Play Store sebanyak 3360 ulasan dianalisis dari 2018 hingga 01 Desember 2023. klasifikasi sentimen terbagi menjadi tiga kategori: positif, negatif, dan netral. Penggunaan Google Colab sebagai alat utama dalam pengolahan data dan implementasi model klasifikasi menawarkan efisiensi dalam eksperimen. Hasil evaluasi menunjukkan bahwa ketiga metode analisis sentimen Random Forest, SVM, dan Naïve Bayes mencapai akurasi tinggi pada evaluasi ulasan aplikasi MyPertamina. Random Forest menonjol dengan akurasi 99.77%, sementara SVM dan Naïve Bayes juga memberikan performa yang baik, masing-masing mencapai 99.31% dan 90.24%. Nilai Precision, Recall, dan F1-Score yang optimal pada ketiga metode mengindikasikan keefektifan mereka dalam menganalisis sentimen ulasan pengguna.
Identifying key patterns of college student’s background through exploratory data analysis Jabir, Sitti Rahmah; Darwis, Herdianti
Journal of Intelligent Decision Support System (IDSS) Vol 9 No 1 (2026): March: Intelligent Decision Support System (IDSS)
Publisher : Institute of Computer Science (IOCS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35335/idss.v9i1.332

Abstract

The declining of student interest had forced universities to examine the characteristics of each student. According to higher education statistics on the number of new students, fluctuating values ​​have been found in recent years. Several research used exploratory data analysis (EDA) approach to analyze new student admissions data. EDA is offered a summary of the dataset analysis and preliminary findings. There are variables decided to be dropped because consisted high number of missing values. On the other hand, some data filled with mean and mode because the number of missing not more than 20%. The missing values in each of attribute might be cleaned using another way. The admission team in university might encourage the registrants to complete and input correct data to the system. Based on the visualization, we found that some college students applied to university from several background of area, demographic and etc. The marketing division might apply another strategy is area had small number of college which is Kalimantan. Public health, computer science and insutry technology are major that have potential to be promoted due to the job prospects.
Co-Authors Adela Regita Azzahra Adnan, Adam Aji Prasetya Wibawa Alfitriana Riska Amaliah, Tazkirah Amelia, Ayu Annisa Febrianti Arfah, Jumrayanti Asfar, A. Muh. Fitrah Astuti, Wistiani Atmajaya, Dedy Atussaliha, Nur Almar' Azwan Triyadi Backar, Sunarti Passura Basri, Haerunnisa Basri, Nur Anisa Dian Fitrah Yusti Randana Harlinda, Harlinda Hasanuddin, Tasrif Haviluddin Haviluddin Irawati Irawati jabir, sitti rahmah Lilis Nur Hayati lilis nurhayati Lokapitasari Belluano, Poetri Lestari Magfirah, Magfirah Mardiyyah Hasnawi Moleo, Alif Safa Munaf, Adryan Dwiprawira Nafalski, Andrew Nia Kurniati Nur wahidah Purnawansyah Purnawansyah Purnawansyah Puspitasari, Rahma Rachman Manga, Abdul Rahma, Reyna Aprilia Rahmasari, Putri Aulia Raja, Roesman Ridwan Salim, Yulita Samir, Mutmainnah Sastra, Ramdan Siska Anraeni Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Umar, Fitriyani Veithzal Rivai Zainal Wanaspati, Nugraha Wati, Silmi Nur Zaskia Widyawati, Dewi Zahirah, Dinna