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All Journal ComEngApp : Computer Engineering and Applications Journal Syntax Jurnal Informatika Jurnal Ilmu Komputer dan Agri-Informatika SITEKIN: Jurnal Sains, Teknologi dan Industri Jurnal Informatika Jurnal CoreIT JURNAL MEDIA INFORMATIKA BUDIDARMA JIEET (Journal of Information Engineering and Educational Technology) Indonesian Journal of Artificial Intelligence and Data Mining Seminar Nasional Teknologi Informasi Komunikasi dan Industri JURNAL INSTEK (Informatika Sains dan Teknologi) Jurnal Informatika Universitas Pamulang Sebatik Jurnal Teknoinfo ICETIA Jurnal Nasional Komputasi dan Teknologi Informasi IJISTECH (International Journal Of Information System & Technology) JURIKOM (Jurnal Riset Komputer) Informatika : Jurnal Informatika, Manajemen dan Komputer Building of Informatics, Technology and Science Zonasi: Jurnal Sistem Informasi Jurnal Informatika Ekonomi Bisnis Jurnal Tekinkom (Teknik Informasi dan Komputer) JOURNAL OF INFORMATION SYSTEM RESEARCH (JOSH) Journal of Computer System and Informatics (JoSYC) Jurnal Sistem Komputer dan Informatika (JSON) JUKI : Jurnal Komputer dan Informatika IJISTECH Information System Journal (INFOS) Bulletin of Computer Science Research KLIK: Kajian Ilmiah Informatika dan Komputer JUSTIN (Jurnal Sistem dan Teknologi Informasi) Bulletin of Information Technology (BIT) Knowbase : International Journal of Knowledge in Database Malcom: Indonesian Journal of Machine Learning and Computer Science Jurnal Sains dan Informatika : Research of Science and Informatic Jurnal Informatika Ekonomi Bisnis
Suwanto Sanjaya
Universitas Islam Negeri Sultan Syarif Kasim Riau
Aerospace Engineering Agriculture, Biological Sciences & Forestry Humanities Automotive Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Mathematics Neuroscience Social Sciences Transportation Other

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Classification of Palm Oil Ripeness Level using DenseNet201 and Rotational Data Augmentation Nabyl Alfahrez Ramadhan Amril; Yanto, Febi; Elvia Budianita; Suwanto Sanjaya; Fadhilah Syafria
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1937

Abstract

Indonesia is a country in Southeast Asia with the largest palm oil production in the world. Based on Indonesian Central Statistics Agency data, in 2022 Indonesia produced 46,8 million Tons of Crude Palm Oil (CPO). To produce a high-quality oil, palm oil fruit must be harvested in an optimal condition. But, even a experienced and trained person found it difficult to identify whether the fruit is ripe or raw. In this research theres two type of classification which is ripe and raw, this is because palm oil milling factory only accept pure ripe palm oil fruit and not half ripe or almost ripe. The data that is used in this reseacrh was collected from two sources, the first source is from https://www.kaggle.com/datasets/ahmadfathan/kematangansawit and the second source was collected manually by going to palm oil plantation. The total of data that is used for this research is 1000 data and 1000 augmented data. Dense Convolutional Network (DenseNet) that is used in this research is a CNN architecture that was first introduced in 2017. Compared to DenseNet121 and DenseNet169, DenseNet201 is proven to have a higher level of accuracy. The 90:10 data scheme succeeded in getting the highest accuracy with a total accuracy of 97.50% with a learning rate of 0.001 and a dropout of 0.01
Deep Learning Menggunakan Algoritma Xception dan Augmentasi Flip Pada Klasifikasi Kematangan Sawit Masaugi, Fathan Fanrita; Yanto, Febi; Budianita, Elvia; Sanjaya, Suwanto; Syafria, Fadhilah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1938

Abstract

Palm oil is an important commodity in Indonesia, especially as Indonesia is the highest palm oil exporting country in the world. Ripe palm fruit is marked by a change in color of the fruit from black to reddish yellow. Apart from that, immature palm fruit has a negative and significant effect on CPO production. The data collection process was carried out by directly taking pictures of palm fruit on oil palm plantations and data obtained from Kaggle. The total amount of data is 1000 images and 1000 data resulting from flip augmentation. The Xception algorithm is an algorithm in deep learning which stands for Extreme version of Inception. This combination was then proven to provide better accuracy in classifying images from a dataset. The optimizer used is the optimizer in TensorFlow, namely Adam (Adaptive Moment Estimation) using learning rate and dropout values. Images of mature and immature palm oil were classified using the Xception algorithm with augmented and without augmented data. In addition, experiments were carried out by changing the parameter values ??of learning rate to 0.1, 0.01, 0.001 and dropout to 0.1, 0.01, 0.001. It was found that the data division was (90;10) with the best accuracy reaching 95%. Test parameters carried out by trialling were proven to increase accuracy when compared to without using parameters and flip augmentation. The best accuracy of the Xception model is 95% on augmented data with a learning rate of 0.001 and a dropout of 0.1.
Implementasi VGG 16 dan Augmentasi Zoom Untuk Klasifikasi Kematangan Sawit Mazdavilaya, T Kaisyarendika; Yanto, Febi; Budianita, Elvia; Sanjaya, Suwanto; Syafria, Fadhilah
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 6 (2024): Juni 2024
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i6.1940

Abstract

Indonesia is a country that has very abundant palm oil plantations and makes palm oil one of the largest export commodities in Indonesia. Fruit maturity on oil palms has a significant influence on palm oil and kernel production. The level of ripeness in palm oil fruit can affect several contents in it, such as tocopherol content, yield and FFA. The classification will be divided into 2 classes, namely between ripe and immature fruit with data on 500 images of ripe fruit and 500 images of immature fruit, data taken from the Kaggle site and private gardens taken using a cellphone camera. The data that has been obtained is augmented which is useful for enriching the data to make it more abundant. Data augmentation uses zoom augmentation and makes the original 1000 data increase to 2000 data. The model used is VGG 16 which is part of deep learning. The existing dataset is then preprocessed, resized and rescaled, then divides the data into 3, namely train, test and valid data. After dividing the data, then carry out the classification process with VGG 16 and set the hyperparameters after that the model will learn with 20 epochs. The model will learn with 57 schemes to compare and find highest accuracy. After the model has finished learning, it is evaluated using a confusion matrix. The results obtained were that the 90:10 data division using data augmentation with a learning rate of 0.01 and a dropout of 0.001 obtained the best accuracy, reaching 93.8%.
Optimalisasi Convolutional Neural Network Menggunakan Augmentasi dan Hyperparameter untuk Klasifikasi Daging Sapi dan Daging Babi Jasril, Jasril; Sanjaya, Suwanto
JUSTIN (Jurnal Sistem dan Teknologi Informasi) Vol 12, No 4 (2024)
Publisher : Jurusan Informatika Universitas Tanjungpura

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26418/justin.v12i4.80337

Abstract

Tujuan penelitian ini adalah untuk menentukan model optimal pada klasifikasi sapi dan babi dengan menerapkan augmentasi data serta hyperparameter pada Convolutional Neural Network (CNN) arsitektur EfficientNet-B0. Data citra daging sapi dan daging babi yang diambil langsung dari beberapa pasar yang ada di kota Pekanbaru. Data diambil menggunakan kamera DSLR dan kamera smartphone dengan jarak antara 10cm sampai 15cm dan pencahayaan menyesuaikan dengan kondisi cahaya pada lingkungan pasar. Proses pelatihan dan pengujian model klasifikasi menggunakan beberapa skenario yaitu kombinasi pembagian data, jenis dataset, optimizer, fungsi aktivasi, dan learning rate. Berdasarkan hasil pengujian, model klasifikasi yang memiliki nilai akurasi tertinggi adalah 0,93 yaitu model dengan skenario jenis dataset gabungan (dataset original ditambah dengan dataset hasil augmentasi) dengan pembagian data 90% data latih dan 10% data uji. Hasil pengujian akurasi tertinggi menunjukkan model tidak overfitting, tetapi masih ada beberapa data citra daging sapi yang diklasifikasikan menjadi daging babi ataupun oplosan, sehingga perlu dilakukan penelitian lebih lanjut untuk meminimalkan masalah tersebut karena sebagai seorang muslim harus memastikan daging sapi yang dimakan adalah benar daging sapi.
Klasifikasi Sentimen pada Dataset Terbatas Menggunakan Random Forest dan Word2Vec Fitri, Dina Deswara; Agustian, Surya; Pizaini, Pizaini; Sanjaya, Suwanto
Journal of Computer System and Informatics (JoSYC) Vol 6 No 1 (2024): November 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josyc.v6i1.6246

Abstract

Sentiment measurement of public opinion on social media is essential for understanding societal views on various issues, including public figures and political events. This research explores the effectiveness of the Random Forest algorithm with Word2Vec-based word representation for sentiment classification on a limited dataset. The case study involves tweets regarding Kaesang Pangarep as the Chairman of PSI, supplemented by external data related to Covid-19 and general topics. The dataset was processed using cleaning techniques, case folding, stopword removal, stemming, and tokenization. Words in the dataset were represented using the Word2Vec model with a Continuous Bag of Words (CBOW) architecture and a vector dimension of 500. Random Forest was employed to classify sentiment into positive, negative, or neutral categories. In the initial phase, the model was trained using 300 samples per label; however, the results showed unsatisfactory performance with an F1-Score of 49.00% and an accuracy of 50.00%. To improve performance, the dataset was expanded by adding 900 samples from Kaesang and 1,080 samples from external topics. The final results indicated an improvement with an F1-Score of 49.89%, an accuracy of 58.29%, precision of 49.16%, and recall of 56.47%. This research confirms that the use of Random Forest with word representation from Word2Vec can enhance sentiment classification performance, even with a limited dataset, and contributes to the development of sentiment analysis techniques in the field of machine learning.
Klasifikasi Kelayakan Air Minum dengan Backpropagation Neural Network Berbasis Penanganan Missing Value dan Normalisasi Kurniawan, Saifur Yusuf; Sanjaya, Suwanto; Vitriani, Yelfi; Afrianty, Iis
Journal of Information System Research (JOSH) Vol 6 No 1 (2024): Oktober 2024
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/josh.v6i1.5871

Abstract

The issue of drinking water quality and its suitability for human consumption represents a significant concern in contemporary society, particularly in the context of maintaining public health. The existing research on the classification of drinking water eligibility has yet to yield conclusive results. The objective of this research is to utilize the backpropagation neural network method to categorize drinking water feasibility data, thereby ensuring that the water consumed meets established safety standards. The data utilized in this study were obtained from an open repository and encompass a total of 3,276 data points. The data set comprises nine water quality parameter attributes, namely pH, hardness, solids, chloramines, sulfate, conductivity, organic carbon, trihalomethanes, and turbidity. The data underwent a series of pre-processing steps, including the removal of missing values, the replacement of missing values with the average value of the attribute, and normalization using the MinMax Scaler and Z-score methods. The artificial neural network architecture comprises three principal components: input, hidden, and output neurons. The optimal architecture scenario is [9; 17; 15; 10; 1], comprising nine input neurons, 17 neurons in the initial hidden layer, 15 neurons in the second hidden layer, 10 neurons in the third hidden layer, and a single output neuron. The evaluation results demonstrate that this model effectively classifies drinking water eligibility data with an accuracy rate of 0.6579. However, the results indicate that the accuracy achieved requires further improvement for more reliable applications. These findings illustrate the promising potential of the BPNN method in classifying drinking water quality data.
Pengembangan Aplikasi Pendeteksi Daging Sapi dan Babi Menggunakan Deep Learning Arsitektur EfficientNet-B6 Berbasis Android Pangestu, Yoga; Sanjaya, Suwanto; Jasril; Agustian, Surya; Safaat, Nazruddin
Jurnal Informatika Ekonomi Bisnis Vol. 7, No. 2 (June 2025)
Publisher : SAFE-Network

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37034/infeb.v7i2.1195

Abstract

The advancement of digital technology has generated a demand for applications that assist the public in ensuring the halal status of food products, particularly in distinguishing between beef and pork. This study aims to develop an Android-based application for detecting beef and pork using Deep Learning methods with the EfficientNet-B6 architecture, employing the eXtreme Programming software development approach. The image classification model utilizes a Convolutional Neural Network architecture integrated into a Python-based server, while the user interface is developed with Java in Android Studio. System testing was conducted using black-box methods on several Android devices, with varying room conditions and meat types. The results show that the application can classify meat with an accuracy of 66.7%, considering room conditions such as light and dark environments, and meat types including fatty and non-fatty. This application provides fast response times and a user-friendly interface. This application is expected to enable users to independently and efficiently verify the halal status of meat, thereby supporting the needs of Muslim consumers in the digital era.
Penggunaan Convolutional Neural Network NASNetLarge Dalam Klasifikasi Citra Daging Babi dan Sapi Aqilah, M Alfandri; Jasril; Sanjaya, Suwanto; Insani, Fitri
Bulletin of Computer Science Research Vol. 5 No. 4 (2025): June 2025
Publisher : Forum Kerjasama Pendidikan Tinggi (FKPT)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47065/bulletincsr.v5i4.666

Abstract

The adulteration of beef with pork is a serious issue in Indonesia, particularly for Muslim consumers who are required to consume halal products. According to a Kompas (2020) report, a case of meat adulteration involving 100 kilograms of mixed meat sold as beef was discovered in Tangerang City. This practice not only violates religious laws but also poses threats to public health and consumer trust. To address this challenge, this study adopts a deep learning approach using NASNetLarge for the classification of pork, beef, and mixed meat images. Unlike previous research that utilized EfficientNet-B2 and achieved an accuracy of 98.23%, this study’s NASNetLarge approach produced a comparably competitive accuracy of 98.03%. The dataset used consists of 1,932 images sourced from the Kaggle platform, which were processed through preprocessing and augmentation stages. The data were then split into two distribution scenarios: the entire dataset and a balanced class dataset with 90:10 and 80:20 ratios. Evaluation results show that the best parameter combination was achieved in the first scenario with a 90:10 ratio using augmented images, a learning rate of 0.001, 128 dense units, and the Adam optimizer. The model achieved the highest accuracy of 98.03%, with a precision of 98.63%, recall of 98.40%, and an F1-score of 98.50%. These results indicate that NASNetLarge is effective in accurately and consistently classifying meat images. Image augmentation significantly improved model performance, and the 90:10 data ratio yielded more optimal results compared to 80:20. These findings have the potential to support food surveillance efforts by enabling rapid and accurate detection of meat adulteration.
EVALUASI PERBANDINGAN PERFORMANSI LVQ 1, LVQ 2, DAN LVQ 3 DALAM KLASIFIKASI JENIS KELAMIN MENGGUNAKAN TULANG TENGKORAK DARMILA; IIS AFRIANTY; SUWANTO SANJAYA; RAHMAD ABDILLAH; IWAN ISKANDAR; FADHILAH SYAFRIA
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 7 No 2 (2022): OCTOBER
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v7i2.32659

Abstract

Klasifikasi merupakan teknik pengelompokkan data sesuai dengan karakteristik data yang telah ditentukan. Hasil performansi akurasi dapat menjadi ukuran keakuratan metode yang digunakan dalam proses klasifikasi. Teknik pengambilan data yang tidak sesuai dapat mengurangi hasil akurasi. Pada penelitian ini menggunakan metode Learning Vector Quantization (LVQ) 1, 2, dan 3 untuk melihat keakuratan metode klasifikasi dengan menggunakan teknik pengambilan data sampling. Data yang digunakan merupakan data pengukuran tulang tengkorak laki-laki dan perempuan yang berjumlah 2524 data. Pada LVQ 1 mendapatkan akurasi terbaik yaitu 91.39% dengan learning rate 0.1, 0.4, 0.7, 0.9. LVQ 2 mendapatkan akurasi terbaik 77.05% dengan learning rate 0.9 dan window 0.2. LVQ 3 mendapatkan akurasi terbaik yaitu 80.04% dengan learning rate 0.7, window 0.1, dan epsilon 0.3. Hal ini menunjukkan bahwa LVQ 1 lebih tepat untuk diterapkan terhadap multi-fitur pada dataset William W. Howells Craniometric dibandingkan LVQ 2 dan LVQ 3.
PERBANDINGAN PERFORMANSI DENGAN METODE CORRELATION BASED FEATURE SELECTION PADA LVQ 2 SURYA ADITYA GD; IIS AFRIANTY; SUWANTO SANJAYA; RAHMAD ABDILLAH; LESTARI HANDAYANI; FITRI INSANI
Jurnal INSTEK (Informatika Sains dan Teknologi) Vol 8 No 1 (2023): APRIL
Publisher : Department of Informatics Engineering, Faculty of Science and Technology, Universitas Islam Negeri Alauddin, Makassar, Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24252/instek.v8i1.37301

Abstract

Melakukan sebuah penelitian diperlukannya mengidentifikasi sebuah data yang sesuai dengan melakukan sebuah klasifikasi. Pengaruh dalam mendapatkan hasil akurasi yang maksimal dengan menentukan teknik penelitian secara tepat melalui proses klasifikasi. Pada penelitian ini melakukan perbandingan peningkatan performansi akurasi akurasi LVQ 2 dengan mengimplementasikan Correlation Based Feature Selection (CFS) pada dataset bertujuan keakuratan pengambilan data sampel dengan metode klasifikasi. Data parameter tulang tengkorak yang digunakan yaitu data pria dan wanita dengan jumlah data 2524 dan fitur 82. Penelitian LVQ 2 tanpa CFS dengan nilai learning rate (α) = 0.9 dan window 0.2 yang akurasi tertingginya memperoleh sebesar 77.05%, dan menggunakan CFS pada nilai α = 0.9 dan window = 0.3 hasil akurasi tertinggi yaitu 82,51%. Hal ini menunjukkan bahwa LVQ 2 menggunakan CFS sangat direkomendasikan baik dari segi performansi terhadap pada dataset Tengkorak dibandingkan LVQ 2 tanpa menggunakan CFS.
Co-Authors Abdussalam Al Masykur Adrian Maulana Afiana Nabilla Zulfa Ahmad Fauzan Ahmad Paisal Ahmad, Rizmah Zakiah Nur Al Fiqri, M. Faiz Alwis Nazir Alwis Nazir Alwis Nazir Alwiz Nazir Amalia Hanifah Artya Annisa Putri Aqilah, M Alfandri Arif Mudi Priyatno Ariq At-Thariq Putra Aulia Ramadhani Cut Lira Kabaatun Nisa Darmila Deny Ardianto Dodi Efendi efni humairah Eka Pandu Cynthia, Eka Pandu Elin Haerani Elvia Budianita Erni Rouza, Erni Ersad Alfarsy Absar, Ersad Alfarsy Fadhilah Syafria Fadhilla Syafria Fakhrezi, Muhammad Dzaki Febi Yanto Felian Nabila Fitri Insani Fitri Insani Fitri Insani (Scopus ID: 57190404820) Fitri, Dina Deswara Gusrifaris Yuda Alhafis Gusti, Siska Kurnia Hafez Almirza Harni, Yulia Hartini Hartini Iis Afrianty Iis Afrianty Iis Afrianty iis afrianty Ikhwanul Akhmad DLY Irman Hermadi Isnan Mellian Ramadhan Israldi, Tino Iwan Iskandar Iwan Iskandar Jasril Jasril Jasril Jasril Jasril Jasril Karina Julita Kurnia Rahman, Fikri Kurniawan, Saifur Yusuf Lestari Handayani Lestari Handayani Lestari Handayani Lia Anggraini Lola Oktavia M. Fadil Martias Masaugi, Fathan Fanrita Maulana Junihardi Mazdavilaya, T Kaisyarendika Megawati Megawati Morina Lisa Pura Muhammad Affandes Muhammad Fikry Muhammad Irfan Syah Muhammad Irsyad Muhammad Irsyad Nabyl Alfahrez Ramadhan Amril Nazir, Alwis Nazruddin Safaat Nazruddin Safaat H Negara, Benny Sukma Novi Yanti Novriyanto Novriyanto Novriyanto Pangestu, Yoga Pizaini Pizaini Puspa Melani Almahmuda Putri Ayuni, Desy Radili, Adi Rahma Shinta Rahmad Abdillah Rahmad Abdillah Ramu Will Sandra Reski Mai Candra Reski Mai Candra Reski Mei Candra Riska Yuliana Saputra, Nugroho Wahyu Sarah Lasniari Sarah Lasniari Shahira, Fayza Siska Kurnia Gusti Siska Kurnia Gusti siska kurnia gusti Sugandi, Hatami Karsa SURYA ADITYA GD Surya Agustian Syaputra, Muhammad Dwiky Ulfah Adzkia Vitriani, Yelfi Yani, Susmi Syahfrida Yelfi Vitriani Yeni Fariati Yusra Yusra, Yusra Yusril Hidayat