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All Journal Jurnal Ilmu Komputer dan Informasi IPTEK The Journal for Technology and Science Techno.Com: Jurnal Teknologi Informasi Bulletin of Electrical Engineering and Informatics Register: Jurnal Ilmiah Teknologi Sistem Informasi JOIV : International Journal on Informatics Visualization ILKOM Jurnal Ilmiah Journal TEPAT: Teknologi Terapan untuk Pengabdian Masyarakat Jurnal Penelitian Enjiniring Fakultas Teknik Unhas Jurnal Teknologi Elekterika Journal Cerita: Creative Education of Research in Information Technology and Artificial Informatics Indonesian Journal of Electrical Engineering and Computer Science Jurnal Minfo Polgan (JMP) Jurnal Ilmiah Sistem Informasi dan Teknik Informatika (JISTI) Jurnal Pengabdian Mandiri Malcom: Indonesian Journal of Machine Learning and Computer Science Inspiration: Jurnal Teknologi Informasi dan Komunikasi
Ingrid Nurtanio
Universitas Hasanuddin
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics Public Health Social Sciences Other

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Penentuan Kualitas Kopra Berbasis Citra Kontur Menggunakan Metode Canny Edge Detection: Determination of Copra Quality Based on Contour Image Using the Canny Edge Detection Method Nugroho, Chandra Wisnu; Nurtanio, Ingrid; Jalil, Abdul
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 1 (2025): MALCOM January 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i1.1823

Abstract

UD Cendrawasih, yang berlokasi di Kelurahan Motoboi Kecil, Kotamobagu Selatan, Sulawesi Utara, adalah usaha jual beli kopra yang telah beroperasi lama. Proses penilaian kualitas kopra saat ini masih manual melalui inspeksi visual, yang meskipun andal, sering kali menghadirkan subjektivitas dan kurang konsisten. Penelitian ini bertujuan mengotomatisasi dan meningkatkan akurasi penilaian kualitas kopra menggunakan metode Canny Edge Detection dengan model Canny-Inception. Model ini mengklasifikasikan kopra menjadi tiga kelas: "basah" (kadar air tinggi), "kering" (kopra yang telah dikeringkan), dan "berjamur" (ditandai oleh warna dan bau). Data dibagi dalam tiga model: Model A (80:10:10), Model B (70:20:10), dan Model C (60:30:10). Hasil penelitian menunjukkan Model C memberikan performa terbaik dengan akurasi validasi 87,50% pada epoch ke-9 dan validation loss 40,27%. Analisis menggunakan Confusion Matrix mengungkapkan Model A unggul pada kelas basah (81%), Model B pada kelas kering (62%), dan Model C pada kelas berjamur (76%). Dengan akurasi keseluruhan 87,50%, Model C dinilai paling efektif untuk klasifikasi kualitas kopra secara akurat dan konsisten.
Klasifikasi Sapi Perah dan Non-Perah Menggunakan Algoritma Convolutional Neural Network: Classification of Dairy and Non-Dairy Cattle Using the Convolutional Neural Network Algorithm Maramis, Leonard; Nurtanio, Ingrid; Zainuddin, Hazriani
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 5 No. 2 (2025): MALCOM April 2025
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v5i2.1824

Abstract

Sapi merupakan salah satu hewan ternak utama di Indonesia yang terdiri dari sapi perah dan sapi potong. Di Kotamobagu dan Bolaang Mongondow Raya (BMR), peternakan sapi berkembang pesat seiring dengan meningkatnya daya beli masyarakat dan nilai jual sapi yang tinggi. Namun, transaksi jual-beli sapi masih menghadapi kendala, terutama dalam membedakan jenis sapi yang dapat menyebabkan kesalahan dan potensi penipuan. Penelitian ini bertujuan untuk mengimplementasikan algoritma Convolutional Neural Network (CNN) dengan arsitektur Xception dalam klasifikasi sapi perah dan non-perah. Proses penelitian mencakup pengumpulan data citra sapi, pelabelan, serta pelatihan model CNN untuk mengenali karakteristik fisik masing-masing jenis sapi. Hasil pengujian menunjukkan bahwa model Xception mencapai akurasi 96% dengan pembagian dataset 80:20, membuktikan kemampuannya dalam mengenali pola visual dengan baik. Temuan ini menunjukkan bahwa CNN, khususnya dengan arsitektur Xception, dapat menjadi alat yang efektif dalam identifikasi jenis sapi, sehingga berpotensi meningkatkan keamanan dan keakuratan dalam transaksi ternak. Dengan pengembangan lebih lanjut, sistem ini dapat diintegrasikan dengan teknologi kamera untuk pemantauan otomatis guna mendukung industri peternakan yang lebih modern dan efisien.
Sistem Pakar Berbasis Pohon Keputusan untuk Diagnosa Awal Penyakit Bergejala Demam Febriansyah, Muhammad; Nurtanio, Ingrid; Yusuf, Mukarramah
Jurnal Minfo Polgan Vol. 13 No. 1 (2024): Artikel Penelitian
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/jmp.v13i1.13762

Abstract

Demam merupakan gejala umum yang sering dijumpai pada berbagai jenis penyakit. Sistem pakar dapat membantu melakukan diagnosa awal penyakit bergejala demam agar penderita dapat mengambil tindakan awal yang tepat Pada penelitian ini dibangun sistem pakar diagnosa awal penyakit dengan gejala demam dengan mengimplementasikan pohon keputusan. Data yang digunakan dikumpulkan dari literatur dan divalidasi oleh pakar (dokter). Sistem pakar pada penelitian ini dibangun berbasis web. Pengujian dilakukan melalui 3 langkah, yaitu inputan gejala yang sama persis dengan yang terdapat pada pohon keputusan, inputan gejala sama dengan pohon keputusan ditambah gejala dari penyakit yang lain, dan inputan gejala sama dengan pohon keputusan tetapi jumlahnya berkurang. Hasil pengujian sistem pakar untuk penyakit demam ini mencapai tingkat akurasi sebesar 86%.
PENINGKATAN CAPAIAN PEMBELAJARAN MATEMATIKA DI SMP 25 MAKASSAR DENGAN GAME ANDROID Mukarramah Yusuf; Ida R Sahali; Firmansyah J Kusuma; Amil A Ilham; Muhammad Niswar; M Alief F Imran; Christoforus Yohannes; Ingrid Nurtanio; Novy NRA Mokobombang; Ais P Alimuddin; Ady W Paundu
JURNAL PENGABDIAN MANDIRI Vol. 4 No. 8: Agustus 2025
Publisher : Bajang Institute

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

Abstract

Hasil pembelajaran Matematika pada siswa-siswa sekolah masih rendah disebabkan karena berbagai permasalahan, salah satunya adalah cara pembelajaran yang kurang menarik. Pendekatan yang kami lakukan di SMP 25 Makassar adalah pengenalan game Android untuk belajar Matematika. Hasil belajar melalui kegiatan berrmain game menunjukkan terjadi peningkatan skor pencapaian tujuan belajar..
Comparison Architecture of Convolutional Neural Network for Fertility Level of Paddy Soil Detection Natsir, Muh. Syahlan; Suyuti, Ansar; Nurtanio, Ingrid; Palantei, Elyas
JOIV : International Journal on Informatics Visualization Vol 9, No 5 (2025)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.9.5.3342

Abstract

This study proposes to detect the fertility of paddy soil based on texture, the power of Hydrogen (pH), and the amount of production. Fertile paddy soil provides essential nutrients and supports optimal plant growth. Therefore, monitoring and analyzing soil fertility is crucial in agricultural land management, which significantly increases rice yields. Paddy soil is categorized into three parts: very fertile soil, fertile soil, and reasonably fertile soil. This research proposes a new approach to detecting soil fertility levels based on factors that influence soil fertility using the Convolutional Neural Network (CNN) algorithm. There are 558 paddy soil datasets of 178 very fertile datasets, 135 fertile datasets, and 245 quite fertile datasets. In this research, we conducted trials using the CNN, Resnet, Enet, and VGG19 models. According to the test results, the CNN model using the Adam optimizer and a learning rate of 0.001 achieves the highest training accuracy of 0.9687 and validation accuracy of 0.8333. This suggests that this model can accurately identify the fertility of paddy soil, making it easier to calculate the fertility of paddy soil through its use. Future research can expand this study by integrating additional soil parameters, such as nitrogen, phosphorus, potassium levels, and organic matter content, to improve classification accuracy further. Additionally, employing multimodal data sources, such as remote sensing and hyperspectral imaging, could enhance the model's robustness in various environmental conditions. Further optimization of deep learning architectures and Artificial Intelligence (AI) techniques can also provide better interpretability and usability for agricultural stakeholders.
Comparison of Convolutional Neural Network Methods for the Classification of Maize Plant Diseases Abas, Mohamad Ilyas; Syafruddin Syarif; Ingrid Nurtanio; Zulkifli Tahir
Register: Jurnal Ilmiah Teknologi Sistem Informasi Vol 10 No 1 (2024): January
Publisher : Information Systems - Universitas Pesantren Tinggi Darul Ulum

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26594/register.v10i1.3656

Abstract

The focus of this study is the classification of maize images with common rust, gray leaf spot, blight, and healthy diseases. Various models, including ResNet50, ResNet101, Xception, VGG16, and ENet, were tested for this purpose. The dataset used for corn plant diseases is publicly available, and the data were split into separate sets for training, validation, and testing. After processing the data, the following models were identified: the Xception model epoch with an accuracy of 83.74%, the ResNet model with an accuracy of 97.19% at epoch 8/10, the ResNet101 model with an accuracy of 97.55% at epoch 10/10, and the ENet model with an accuracy of 98.69% at epoch 9/1000. ENet exhibited the highest accuracy among the five models at 98.69%. Additionally, ENet achieved an average accuracy of 95.45%, the highest among all tested models, based on the average accuracy in the confusion matrix. This research indicates that ENet performs best at processing data related to maize plant diseases. Consequently, the analysis of maize plant diseases is expected to evolve as a result of this research. Following the implementation of the system's generated model, this research will continue to explore its impact. The intention is to provide a summary of the comparative classification performance of CNN algorithms.
Performance Analysis of Feature Mel Frequency Cepstral Coefficient and Short Time Fourier Transform Input for Lie Detection using Convolutional Neural Network Kusumawati, Dewi; Ilham, Amil Ahmad; Achmad, Andani; Nurtanio, Ingrid
JOIV : International Journal on Informatics Visualization Vol 8, No 1 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.1.2062

Abstract

This study aims to determine which model is more effective in detecting lies between models with Mel Frequency Cepstral Coefficient (MFCC) and Short Time Fourier Transform (STFT) processes using Convolutional Neural Network (CNN). MFCC and STFT processes are based on digital voice data from video recordings that have been given lie or truth information regarding certain situations. Data is then pre-processed and trained on CNN. The results of model performance evaluation with hyper-tuning parameters and random search implementation show that using MFCC as Voice data processing provides better performance with higher accuracy than using the STFT process. The best parameters from MFCC are obtained with filter convolutional=64, kerneconvolutional1=5, filterconvolutional2=112, kernel convolutional2=3, filter convolutional3=32, kernelconvolutional3 =5, dense1=96, optimizer=RMSProp, learning rate=0.001 which achieves an accuracy of  97.13%, with an AUC value of 0.97. Using the STFT, the best parameters are obtained with filter convolutional1=96, kernel convolutional1=5, convolutional2 filters=48, convolutional2 kernels=5, convolutional3 filters=96, convolutional3 kernels=5, dense1=128, Optimizer=Adaddelta, learning rate=0.001, which achieves an accuracy of 95.39% with an AUC value of 0.95. Prosodics are used to compare the performance of MFCC and STFT. The result is that prosodic has a low accuracy of 68%. The analysis shows that using MFCC as the process of sound extraction with the CNN model produces the best performance for cases of lie detection using audio. It can be optimized for further research by combining CNN architectural models such as ResNet, AlexNet, and other architectures to obtain new models and improve lie detection accuracy.
Composition Model of Organic Waste Raw Materials Image-Based To Obtain Charcoal Briquette Energy Potential Saptadi, Norbertus Tri Suswanto; Suyuti, Ansar; Ilham, Amil Ahmad; Nurtanio, Ingrid
JOIV : International Journal on Informatics Visualization Vol 7, No 3 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30630/joiv.7.3.1682

Abstract

Indonesia needs new renewable energy as an alternative to fuel oil. The existence of organic waste is an opportunity to replace oil because it is renewable and contains relatively less air-polluting sulfur. Previous research that has been widely carried out still utilizes coconut shell raw materials, which are increasingly limited in number, so other alternative raw materials are needed. A model is needed to make a formulation that can optimize the composition of organic waste raw materials as a basic ingredient for making briquettes. The research objective was to determine the best raw material composition based on digital image analysis in processing organic waste into briquettes. An artificial intelligence approach with a Convolutional Neural Network (CNN) architecture can predict an effective object detection model. The image analysis results have shown an effective model in the raw material composition of 60% coconut, 20% wood, and 20% adhesive to produce quality biomass briquettes. Briquettes with a higher percentage of coconut will perform better in composition tests than mixed briquettes. The energy obtained from burning briquettes is useful for meeting household fuel needs and meeting micro, small, and medium business industries.
Hybrid Deep Learning Approach For Stress Detection Model Through Speech Signal Chyan, Phie; Achmad, Andani; Nurtanio, Ingrid; Areni, Intan Sari
JOIV : International Journal on Informatics Visualization Vol 7, No 4 (2023)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.7.4.2026

Abstract

Stress is a psychological condition that requires proper treatment due to its potential long-term effects on health and cognitive faculties. This is particularly pertinent when considering pre- and early-school-age children, where stress can yield a range of adverse effects. Furthermore, detection in children requires a particular approach different from adults because of their physical and cognitive limitations. Traditional approaches, such as psychological assessments or the measurement of biosignal parameters prove ineffective in this context. Speech is also one of the approaches used to detect stress without causing discomfort to the subject and does not require prerequisites for a certain level of cognitive ability. Therefore, this study introduced a hybrid deep learning approach using supervised and unsupervised learning in a stress detection model. The model predicted the stress state of the subject and provided positional data point analysis in the form of a cluster map to obtain information on the degree using CNN and GSOM algorithms. The results showed an average accuracy and F1 score of 94.7% and 95%, using the children's voice dataset. To compare with the state-of-the-art, model were tested with the open-source DAIC Woz dataset and obtained average accuracy and F1 scores of 89% and 88%. The cluster map generated by GSOM further underscored the discerning capability in identifying stress and quantifying the degree experienced by the subjects, based on their speech patterns
Deepfake Detection in Videos Using Long Short-Term Memory and CNN ResNext Abidin, Muhammad Indra; Nurtanio, Ingrid; Achmad, Andani
ILKOM Jurnal Ilmiah Vol 14, No 3 (2022)
Publisher : Prodi Teknik Informatika FIK Universitas Muslim Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33096/ilkom.v14i3.1254.178-185

Abstract

Deep-fake in videos is a video synthesis technique by changing the people’s face in the video with others’ face. Deep-fake technology in videos has been used to manipulate information, therefore it is necessary to detect deep-fakes in videos. This paper aimed to detect deep-fakes in videos using the ResNext Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms. The video data was divided into 4 types, namely video with 10 frames, 20 frames, 40 frames and 60 frames. Furthermore, face detection was used to crop the image to 100 x 100 pixels and then the pictures were processed using ResNext CNN and LSTM. The confusion matrix was employed to measure the performance of the ResNext CNN-LSTM algorithm. The indicators used were accuracy, precision, and recall. The results of data classification showed that the highest accuracy value was 90% for data with 40 and 60 frames. While data with 10 frames had the lowest accuracy with 52% only. ResNext CNN-LSTM was able to detect deep-fakes in videos well even though the size of the image was small.
Co-Authors A. Ais Prayogi Alimuddin A. Marimar Muchtamar A.Ais Prayogi Abdul Jalil Abidin, Muhammad Indra Adnan Adnan Adnan Adnan Adnan Adnan Ady W Paundu Ady Wahyudi Ady Wahyudi Paundu Ady Wahyudi Paundu Ahmad Rifaldi Ais P Alimuddin Ais Prayogi Alimuddin Alimuddin, A.Ais Prayogi Amil A Ilham Amil A Ilham Amil A. Ilham Amil Ahmad Ilham Amil Ahmad Ilham Amirullah, Indrabayu Andani Achmad Andi Syam Aswandi Ansar Suyuti Anugrayani Bustamin Anugrayani Bustamin Anugrayani Bustamin Anugrayani Bustamin Areni, Intan Sari Astri Oktianawaty Aulia Darnilasari Bustamin, Anugrahyani Bustamin, Anugrayani Chandra Wisnu Nugroho Christoforus Yohanes Dewi Kusumawati, Dewi Elly Warni Elly Warni elly warni Febriansyah, Muhammad Firmansyah J Kusuma Fransisca J Pontoh Handoyo, Alif Tri Hazriani, Hazriani I Ketut Eddy Purnama Ida Ayu Putu Sri Widnyani Ida R Sahali Imran Taufiq Indra Bayu Indrabayu - Indrabayu . Indrabayu Indrabayu Indrabayu Indrabayu Intan Sari Areni Intan Sari Areni Intan Sari Areni Iqra Aswad Iqra Aswad Jayanti Yusmah Sari Leonard Maramis Leonard, Calvin Rinaldy Lika Purwanti M Alief F Imran Mahdaniar, Mahdaniar Marindah, Tyanita Puti Mauridhi Hery Purnomo Mochamad Hariadi Mohamad Ilyas Abas Mokobombang, Novy Nur R A Mokobombang, Novy Nur R.A Muh. Alief Fahdal Imran Oemar Muh. Syahlan Natsir Muhammad Niswar Muhammad Nizwar Mukarramah Yusuf Mukarramah Yusuf Musakkir Musakkir Musyfirah, Kamtina Naufal Khalil Novy NRA Mokobombang Nur Hikmah Nurdin, Arliyanti Nurdin, Winati Mutmainnah Nurhikmayana Janna Palantei, Elyas Paundu, Ady Wahyudi Phie Chyan Rahmat Hardian Putra Rieka Zalzabillah Putri RIFALDI, AHMAD Riny Yustica Dewi Rizka Irianty Siska Anraeni Syafruddin Syarif Syafruddin Syarif Usman Umar Yohannes, Christoforus Yohannes, Chystoporus Yusuf, Mukarramah Zaenab Muslimin Zaenab Muslimin Zahir Zainuddin Zahir Zainuddin Zulkifli Tahir