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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) 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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COMBINATIONS OF FEATURE EXTRACTIONS AND MACHINE LEARNING ALGORITHMS FOR SKIN CANCER CLASSIFICATION Asfar, A. Muh. Fitrah; Hasnawi, Mardiyyah; Darwis, Herdianti
Jurnal Teknik Informatika (Jutif) Vol. 5 No. 6 (2024): JUTIF Volume 5, Number 6, Desember 2024
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2024.5.6.2514

Abstract

One of the most common causes of death worldwide is skin cancer and its incidence is increasing. To achieve optimal treatment and improve clinical outcomes for patients, precision skin cancer detection and classification approaches are required, which can be achieved through the application of feature extraction and machine learning algorithms. The development of such algorithms to identify important patterns from skin cancer image datasets enables early detection and more accurate classification and more effective treatment. Although previous studies have tried to detect skin cancer using feature extraction techniques such as HFF, HOG, and GLCM, some weaknesses still need to be improved. This research aims to combine various feature extraction methods such as Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns and machine learning algorithms such as Support Vector Machine, Random Forest, and Gaussian Naïve Bayes in the classification process between Melanoma and Nevus skin cancers. In this research, the number of datasets used is 17,397 derived from the ISIC Dataset. The results showed that the Histogram Oriented Gradients method with Support Vector Machine algorithm achieved the highest accuracy of 92%. The combination of Gray Level Co-occurrence Matrix and Local Binary Patterns with Random Forest algorithm also achieved an accuracy of 92%, the combination of Gray Level Co-occurrence Matrix, Histogram Oriented Gradients, and Local Binary Patterns with Random Forest algorithm also resulted in an accuracy of 92%. These findings suggest that the combination of various feature extraction methods and machine learning algorithms can improve accuracy in skin cancer classification, which in turn can contribute to early detection and more effective treatment.
Studi Perbandingan Kombinasi GMI, HSV, KNN, dan CNN pada Klasifikasi Daun Herbal Alfitriana Riska; Purnawansyah; Darwis, Herdianti; Astuti, Wistiani
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3210

Abstract

Tumbuhan herbal memiliki banyak variasi yang dapat dikenali melalui ciri uniknya secara visual. Namun, cara ini sulit diterapkan pada tumbuhan yang memiliki ciri hampir sama. Penelitian ini membandingkan kinerja metode K-Nearest Neighbour (KNN) dan Convolutional Neural Network (CNN) dalam klasifikasi fitur daun herbal yang diekstraksi dengan menggunakan Geometric Moment Invariant (GMI) dan Hue Saturation Value (HSV). Dataset yang digunakan adalah dataset citra daun katuk (Sauropus androgynus) dan daun kelor (Moringa oleifera) dengan skenario citra terang dan citra gelap. Pembagian data untuk tiap skenario adalah 80% untuk training dan 20% untuk testing. Metode KNN diuji menggunakan nilai dan evaluasi kinerja KNN dan CNN meliputi accuracy, precision, recall, dan f1-score. Hasil penelitian menunjukkan bahwa CNN tanpa ekstraksi fitur dan CNN dengan kombinasi ekstraksi fitur HSV memperoleh performa terbaik dengan rata-rata nilai precision, recall, f1-score dan accuracy sebesar 98% untuk skenario gelap maupun terang.
Klasifikasi Penyakit Bawang Merah Menggunakan Naive Bayes dan CNN dengan Fitur GLCM Arfah, Jumrayanti; Purnawansyah; Darwis, Herdianti; Sastra, Ramdan
The Indonesian Journal of Computer Science Vol. 12 No. 3 (2023): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v12i3.3236

Abstract

Tanaman bawang merah merupakan salah satu tanaman penting dalam industri pertanian. Penyakit pada tanaman bawang merah dapat mengakibatkan kerugian yang signifikan bagi petani dan produsen. Penelitian ini bertujuan untuk mengklasifikasikan penyakit bawang merah pada daun bawang merah yang disebabkan oleh bercak ungu dan moler. Pengumpulan data citra bawang merah dilakukan secara langsung yang dilanjutkan dengan tahap pre-processing sebelum pengklasifikasian penyakit pada tanaman bawang merah. Algoritma Naive Bayes dan CNN dengan ekstraksi fitur GLCM digunakan dalam penelitian ini untuk melakukan perbandingan klasifikasi antara dua metode tersebut dalam mengklasifikasikan penyakit tanaman bawang merah yaitu bercak ungu dan moler. Hasil pengujian dengan menggunakan citra sebanyak 160 penyakit moler dan 160 penyakit bercak ungu menunjukkan bahwa kedua algoritma klasifikasi Naive Bayes dan CNN dengan ekstraksi fitur GLCM mampu mengklasifikasikan penyakit moler dan penyakit bercak ungu pada daun bawang merah dengan akurasi yang baik sebesar 100%. Onion plants are one of the important crops in the agricultural industry. Diseases in onion plants can result in significant losses for farmers and producers. This research aims to classify onion diseases on onion leaves caused by priole blotch and molāris. The of onion image data colaction was performed directly, followed by a pre-processing stage before classifying diseases in onion plants. The Naive Bayes algorithm and CNN with GLCM feature extraction are used in this study to compare the classification between the two methods in classifying onion diseases. The test results using a total of 160 priole blotch and 160 molāris diseases show that both the Naive Bayes and CNN classification algorithms with GLCM feature extraction are capable of classifying priole blotch and molāris diseases on onion leaves with a perfect accuracy of 100%.
Klasifikasi Daun Herbal Menggunakan K-Nearest Neighbor dan Convolutional Neural Network dengan Ekstraksi Fourier Descriptor Basri, Haerunnisa; Purnawansyah, Purnawansyah; Darwis, Herdianti; Umar, Fitriyani
Jurnal Teknologi dan Manajemen Informatika Vol. 9 No. 2 (2023): Desember 2023
Publisher : Universitas Merdeka Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26905/jtmi.v9i2.10350

Abstract

The number of herbal plants in Indonesia is 30,000, but only about 1,200 plants are used in medicine. The large number of herbal plants makes it difficult for people to distinguish one type of herbal plant from another. From these conditions, this research has conducted tests to compare the performance of the K-Nearest Neighbor (KNN) and Convolutional Neural Network (CNN) methods using Fourier Descriptor (FD) feature extraction on herbal plants, namely moringa (moringa oleifera) and katuk (sauropus androgynus). The amount of data used is 480 data using image conditions, namely dark and light images which are then divided into 20% testing data and 80% training data. Classification is done using the KNN method using 5 distance calculations (Euclidean, Chebyshev, Manhattan, Minkowski, and Hamming) and CNN with FD feature extraction. From the tests that have been carried out, it is found that the use of FD feature extraction for the KNN method produces the best performance on both light and dark image data. While the use of the CNN method, for dark image data, the best accuracy results are obtained with FD feature extraction and CNN. Meanwhile, for bright image data, the best performance accuracy results are obtained in the CNN method without going through feature extraction. Of these three methods, using FD and KNN feature extraction is more recommended because it produces 100% accuracy in moringa and katuk images with light and dark intensity.
Analisis Sentiment Publik Mengenai Neuralink dari Twitter dengan Menerapkan Naïve Bayes: Multinomial, Gaussian, dan Complement Azwan Triyadi; Purnawansyah; Darwis, Herdianti
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4278

Abstract

Elon Musk owns the business Neuralink, which attempts to build brain-machine interfaces. This study categorizes public opinion towards the use of Neuralink goods, including whether people agree (positive), disagree (negative), or feel neither way. Without accessing the Twitter API, the Twint Python Libraries were utilised to retrieve a dataset of 3000 using the keyword “neuralink”. What datasets are included in positive, neutral, or negative categories are designated using RoBERTa. Term Frequency Inverse Document Frequency (TF-IDF) is utilized for feature extraction, while Synthetic Minority Over-sampling Technique (SMOTE) is employed to handle class imbalance. Complement Naive Bayes, achieved accuracy of 81%, followed by Multinomial Naive Bayes, which achieved accuracy of 80%, and Gaussian Naive Bayes, which achieved accuracy of 75%. The model Complement Naïve Bayes was used in this study to attain the maximum accuracy, and accuracy increases when employing SMOTE compared to other Naïve bayes variants.
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.
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 Fajar AR, Muh. Aqil Fitrah Yusti Randana Fitri Rahayu Harlinda, Harlinda Hasanuddin, Tasrif Hasihi, Cholisah Erman Haviluddin Haviluddin Irawati Irawati jabir, sitti rahmah Lilis Nur Hayati lilis nurhayati Lokapitasari Belluano, Poetri Lestari Magfirah, Magfirah Mardiyyah Hasnawi Ming Foey Teng, Ming Foey Moleo, Alif Safa Munaf, Adryan Dwiprawira Nafalski, Andrew Nia Kurniati Nur wahidah Purnawansyah Purnawansyah Purnawansyah Puspitasari, Rahma Rahmasari, Putri Aulia Raja, Roesman Ridwan Salim, Yulita Samir, Mutmainnah Sastra, Ramdan Siska Anraeni Syahrir, Firdaus Abrazawaiz Triyanna Widiyaningtyas Triyanna Widyaningtyas, Triyanna Umalekhoa, Alfi Syahrin Umar, Fitriyani Veithzal Rivai Zainal Wanaspati, Nugraha Wati, Silmi Nur Zaskia Widyawati, Dewi Zahirah, Dinna