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All Journal Katalogis Jurnal Teknologi Informasi dan Ilmu Komputer Jurnas Nasional Teknologi dan Sistem Informasi Sistemasi: Jurnal Sistem Informasi Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) International Journal of Artificial Intelligence Research JURNAL MEDIA INFORMATIKA BUDIDARMA Abdimasku : Jurnal Pengabdian Masyarakat Jurnal Teknik Informatika (JUTIF) Jurnal Indonesia Sosial Teknologi Scientific Journal of Informatics
Affandy Affandy
Program Studi Sistem Informasi Fakultas Ilmu Komputer Universitas Dian Nuswantoro
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Engineering Environmental Science Health Professions Industrial & Manufacturing Engineering Law, Crime, Criminology & Criminal Justice Social Sciences

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ANALISIS GAYA KEPEMIMPINAN DALAM MENINGKATKAN MOTIVASI KERJA PEGAWAI PADA DINAS SOSIAL, TENAGA KERJA DAN TRANSMIGRASI KABUPATEN SIGI Affandy, Affandy
Katalogis Vol 4, No 9 (2016)
Publisher : Katalogis

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (216.423 KB)

Abstract

This research aims at finding out the influence of leadhership style toward the motivation of the employees at social department of labor and transmigration Sigi regency. This was a qualitative research. The informans were taken based on the criteria: authorities, engaged or undergoing the process of leadhership toward the motivation. The data were collected through observation, interview, and documentation. The result of analysis show that motivation of the employees at social department of labor and transmigration Sigi regency was a partisipative leadhership style. This type of leadhershipstyle can give goodcooperation between the leader and the employees and the cooperation among employees. It can be seen from the way the employees consult their job to their leader, the leader’s way to take a decision, how the leader delegates the authority to employees, and the way the leader gives an apportunity to the employees to express their opinion, ideas about their activites nd job. By applying a pertisipative leadhership style, the head of social department of labor and transmigration can improve the motivation of the employees by providing a comfort atmoshere and giving confidence and responsibility in doing the job.
Analisis Metode Smoote pada Klasifikasi Penyakit Jantung Berbasis Random Forest Tree Yulianto, Satria Pradana Rizki; Fanani, Ahmad Zainul; Affandy, Affandy; Aziz, Mochammad Ilham
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7712

Abstract

Cardiovascular disease is the number one cause of death globally. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels. At present, there are many predictive tools that use machine learning as a basis, including predictions on heart disease in particular. There are many methods in machine learning to predict heart disease, as well as many parameters to look for to find the highest level of accuracy. This study, aims to obtain the best methods and parameters for the classification of heart disease.
Enhancing Machine Learning Accuracy in Detecting Preventable Diseases using Backward Elimination Method Dliyauddin, Muhammad; Shidik, Guruh Fajar; Affandy, Affandy; Soeleman, M. Arief
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 1 (2024): Januari 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i1.7073

Abstract

In the current landscape of abundant high-dimensional datasets, addressing classification challenges is pivotal. While prior studies have effectively utilized Backward Elimination (BE) for disease detection, there is a notable absence of research demonstrating the method's significance through comprehensive comparisons across diverse databases. The study aims to extend its contribution by applying BE across multiple machine learning algorithms (MLAs)Nave Bayes (NB), k-Nearest Neighbors (KNN), and Support Vector Machine (SVM)on datasets associated with preventable diseases (i.e. heart failure (HF), breast cancer (BC), and diabetes). This study aims to elucidate and recommend significant differences observed in the application of BE across diverse datasets and machine learning (ML) methods. This study conducted testing on four distinct datasetsraisin, HF, BC, and early-stage diabetes risk prediction datasets. Each dataset underwent evaluation with three MLAs: NB, KNN, and SVM. The application of BE successfully eliminated non-significant attributes, retaining only influential ones in the model. In addition, t-test results revealed a significant impact on accuracy across all datasets (p-value < 0.05). In specific algorithmic evaluations, SVM exhibited the highest accuracy for the raisin dataset at 87.22%. Additionally, KNN attained the utmost accuracy in the heart failure dataset with an accuracy of 86.31%. In the breast cancer dataset, KNN again excelled, achieving an accuracy of 83.56%. For the diabetes dataset, KNN proved the most accurate, reaching 96.15%. These results underscore the efficacy of BE in enhancing the execution of MLAs for disease detection.
Exploiting Silhouette Principle Component For Dimension Reduction In Breast Ultrasound Images Classification Kartikadarma, Etika; Fanani, Ahmad Zainul; Pujiono, Pujiono; Affandy, Affandy; Wulandari, Sari Ayu
International Journal of Artificial Intelligence Research Vol 8, No 1 (2024): June 2024
Publisher : Universitas Dharma Wacana

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.29099/ijair.v8i1.1165

Abstract

This paper proposes the use of the Dimensional Reduction method with the Silhouette Principle Component (SPC) Approach to improve the classification of breast ultrasound images. The PCA method is used to reduce the dimensions of medical images to improve classification, with MobileNet-v2 and DenseNet-121 as the optimal classification algorithm choices. The results show that the SPC method succeeded in producing efficient feature representation with data sizes that are almost the same as the original data, while PCA produces greater dimensionality reduction. The SPC model also shows the best performance in terms of accuracy and loss. This research makes a significant contribution to the development of more sophisticated and efficient medical image analysis techniques to support the diagnosis and treatment of breast cancer.
Leveraging Label Preprocessing for Effective End-to-End Indonesian Automatic Speech Recognition Althoff, Mohammad Noval; Affandy, Affandy; Luthfiarta, Ardytha; Satya, Mohammad Wahyu Bagus Dwi; Basiron, Halizah
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 1 (2025): Research Article, January 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i1.14257

Abstract

This research explores the potential of improving low-resource Automatic Speech Recognition (ASR) performance by leveraging label preprocessing techniques in conjunction with the wav2vec2-large Self-Supervised Learning (SSL) model. ASR technology plays a critical role in enhancing educational accessibility for children with disabilities in Indonesia, yet its development faces challenges due to limited labeled datasets. SSL models like wav2vec 2.0 have shown promise by learning rich speech representations from raw audio with minimal labeled data. Still, their dependence on large datasets and significant computational resources limits their application in low-resource settings. This study introduces a label preprocessing technique to address these limitations, comparing three scenarios: training without preprocessing, with the proposed preprocessing method, and with an alternative method. Using only 16 hours of labeled data, the proposed preprocessing approach achieves a Word Error Rate (WER) of 15.83%, significantly outperforming the baseline scenario (33.45% WER) and the alternative preprocessing method (19.62% WER). Further training using the proposed preprocessing technique with increased epochs reduces the WER to 14.00%. These results highlight the effectiveness of label preprocessing in reducing data dependency while enhancing model performance. The findings demonstrate the feasibility of developing robust ASR models for low-resource languages, offering a scalable solution for advancing ASR technology and improving educational accessibility, particularly for underrepresented languages.
Analisis Metode Smoote pada Klasifikasi Penyakit Jantung Berbasis Random Forest Tree Yulianto, Satria Pradana Rizki; Fanani, Ahmad Zainul; Affandy, Affandy; Aziz, Mochammad Ilham
JURNAL MEDIA INFORMATIKA BUDIDARMA Vol 8, No 3 (2024): Juli 2024
Publisher : Universitas Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/mib.v8i3.7712

Abstract

Cardiovascular disease is the number one cause of death globally. Cardiovascular disease is a disease caused by impaired function of the heart and blood vessels. At present, there are many predictive tools that use machine learning as a basis, including predictions on heart disease in particular. There are many methods in machine learning to predict heart disease, as well as many parameters to look for to find the highest level of accuracy. This study, aims to obtain the best methods and parameters for the classification of heart disease.
Enhancing Monkeypox Skin Lesion Classification With Resnet50v2: The Impact Of Pre-Trained Models From Medical And General Domains Azhar, Saifulloh; Syukur, Abdul; Soeleman, M. Arief; Affandy, Affandy; Marjuni, Aris
Jurnal Teknik Informatika (Jutif) Vol. 6 No. 3 (2025): JUTIF Volume 6, Number 3, Juni 2025
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2025.6.3.4486

Abstract

The monkeypox outbreak has emerged as a pressing global health concern, as evidenced by the rising number of cases reported in various countries. This rare zoonotic disease, caused by the Monkeypox virus (MPXV) of the Poxviridae family, is commonly found in Africa. However, since 2022, cases have also spread to various countries, including Indonesia. The dermatological symptoms exhibited by affected individuals vary, with the potential for further transmission through contamination. Early and accurate detection of monkey pox disease is therefore essential for effective treatment. The present study aims to improve the classification of Monkey Pox using the modified Resnet50V2 model, trained using pre-training datasets namely ImageNet and HAM10000, where batch size and learning rate parameters were adjusted. The study achieved high accuracy in distinguishing monkeypox cases, with 98.43% accuracy for Resnet50V2 with pretrained ImageNet and 70.57% accuracy for Resnet50V2 with pretrained HAM10000. Future research will focus on refining these models, exploring hybrid approaches incorporating convolutional neural networks, this advancement contributes to the development of automated early diagnosis tools for monkeypox skin conditions, especially in resource-limited clinical settings where access to dermatology experts is limited.
Pengenalan Computational Thinking Sebagai Metode Problem Solving Kepada Guru dan Siswa Sekolah di Kota Semarang Sukamto, Titien S.; Pertiwi, Ayu; Affandy, Affandy; Syukur, Abdul; Hafidhoh, Nisa'ul; Hidayat, Erwin Yudi
ABDIMASKU : JURNAL PENGABDIAN MASYARAKAT Vol 2, No 2 (2019): Juli 2019
Publisher : LPPM UNIVERSITAS DIAN NUSWANTORO

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1534.132 KB) | DOI: 10.33633/ja.v2i2.51

Abstract

Problem solving merupakan salah satu kemampuan yang sangat dibutuhkan untuk menghadapi persaingan global. Maka dari itu perlu untuk dilatih sedari dini. Melihat pada perkembangan teknologi dan imlu komputer, lahirlah sebuah pendekatan problem solving skill yang dikenal dengan nama Computational Thinking (CT). CT dikembangkan dari konsep dasar ilmu komputer, dengan cara mengabstraksi permasalahan kemudian mengilustrasikan dan menyusun solusi. Mulai tahun 2016, Indonesia secara aktif berpartisipasi dalam Komunitas Bebras dan mengkampanyekan Computational Thinking dengan mengadakan Bebras Challenge bagi siswa sekolah di seluruh Indonesia. Fakultas Ilmu Komputer UDINUS menjadi salah satu Bebras Biro yang ikut sebagai penyelenggara Bebras Challenge di Kota Semarang. Penyuluhan Bebras kepada Guru dimaksudkan untuk mengenalkan skill Computational Thinking ini, sehingga ke depannya setiap guru dapat menyampaikan dan melatih siswanya dalam pengembangan skill problem solving. Penyuluhan diikuti oleh guru perwakilan dari 27 sekolah dasar di Kota Semarang. Sebagai rangkaian kampanye, Bebras Challenge diikuti oleh total 169 siswa dari SD dan SMP di Kota Semarang. Hasil Bebras Challenge, terdapat 1 peserta asal Bebras Biro UDINUS yang berhasil masuk peringkat 3 besar nasional.
Analisis Sentimen Twitter untuk Menilai Opini Terhadap Perusahaan Publik Menggunakan Algoritma Deep Neural Network Hidayat, Erwin Yudi; Hardiansyah, Raindy Wicaksana; Affandy, Affandy
Jurnal Nasional Teknologi dan Sistem Informasi Vol 7 No 2 (2021): Agustus 2021
Publisher : Departemen Sistem Informasi, Fakultas Teknologi Informasi, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/TEKNOSI.v7i2.2021.108-118

Abstract

Dalam menaikkan kinerja serta mengevaluasi kualitas, perusahaan publik membutuhkan feedback dari masyarakat / konsumen yang bisa didapat melalui media sosial. Sebagai pengguna media sosial Twitter terbesar ketiga di dunia, tweet yang beredar di Indonesia memiliki potensi meningkatkan reputasi dan citra perusahaan. Dengan memanfaatkan algoritma Deep Neural Network (DNN), neural network yang tersusun dari layer yang jumlahnya lebih dari satu, didapati hasil analisa sentimen pada Twitter berbahasa Indonesia menjadi lebih baik dibanding dengan metode lainnya. Penelitian ini menganalisa sentimen melalui tweet dari masyarakat Indonesia terhadap sejumlah perusahaan publik dengan menggunakan DNN. Data Tweet sebanyak 5504 record didapat dengan melakukan crawling melalui Application Programming Interface (API) Twitter yang selanjutnya dilakukan preprocessing (cleansing, case folding, formalisasi, stemming, dan tokenisasi). Proses labeling dilakukan untuk 3902 record dengan memanfaatkan aplikasi Sentiment Strength Detection. Tahap pelatihan model dilakukan menggunakan algoritma DNN dengan variasi jumlah hidden layer, susunan node, dan nilai learning rate. Eksperimen dengan proporsi data training dan testing sebesar 90:10 memberikan hasil performa terbaik. Model tersusun dengan 3 hidden layer dengan susunan node tiap layer pada model tersebut yaitu 128, 256, 128 node dan menggunakan learning rate sebesar 0.005, model mampu menghasilkan nilai akurasi mencapai 88.72%. 
Peningkatan Performa Ensemble Learning pada Segmentasi Semantik Gambar dengan Teknik Oversampling untuk Class Imbalance Nugroho, Arie; Soeleman, M. Arief; Pramunendar, Ricardus Anggi; Affandy, Affandy; Nurhindarto, Aris
Jurnal Teknologi Informasi dan Ilmu Komputer Vol 10 No 4: Agustus 2023
Publisher : Fakultas Ilmu Komputer, Universitas Brawijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25126/jtiik.2024106831

Abstract

Perkembangan teknologi dan gaya hidup manusia yang semakin tinggi menghasilkan data-data yang berlimpah. Data-data tersebut dapat berbentuk data yang terstruktur dan tidak terstruktur. Data gambar termasuk dalam data yang tidak terstruktur. Aktifitas dan objek yang terekam dalam suatu gambar beraneka ragam. Secara normal, mata manusia dapat dengan mudah membedakan antara foreground dan background dari suatu gambar, tetapi komputer membutuhkan pembelajaran dalam membedakan keduanya. Segmentasi gambar adalah salah satu bidang dalam computer vision yang membahas bagaimana cara komputer mempelajari dan mengenali segmen dari suatu gambar sesuai label yang ditentukan. Dalam kenyataannya banyak data yang mempunyai class atau label yang tidak seimbang, tentunya akan mempengaruhi tingkat akurasi dari suatu prediksi. Dalam riset ini membahas bagaimana meningkatkan akurasi segmentasi semantik gambar pada metode ensemble learning untuk menangani masalah data yang tidak seimbang dalam segmentasi gambar. Teknik yang digunakan adalah sintetis oversampling sehingga menghasilkan data yang seimbang dan akurasi yang tinggi. Metode ensemble learning yang digunakan adalah Random Forest dan Light Gradien Boosting Machine (LGBM). Dengan menggunakan dataset Penn-Fudan Database for Pedestrian yang mengandung imbalanced class. Penggunaan teknik sintetis oversampling dapat memperbaikki tingkat akurasi pada class minoritas. Pada algoritma random forest mengalami peningkatan akurasi sebesar 37 % sedangkan pada algoritma LGBM meningkat sebesar 41 %. AbstractThe development of technology and the increasingly high lifestyle of humans produce abundant data. These data can be in the form of structured and unstructured data. Image data is included in unstructured data. The activities and objects recorded in a picture are varied. Normally, the human eye can easily distinguish between the foreground and background of an image, but computers need learning to distinguish between the two. Image segmentation is one of the fields in computer vision that discusses how computers learn and recognize segments of an image according to specified labels. In reality, a lot of data has unbalanced classes or labels, of course, it will affect the accuracy of a prediction. This research discusses how to improve the accuracy of image semantic segmentation in the ensemble learning method to deal with the problem of unbalanced data in image segmentation. The technique used is synthetic oversampling so as to produce balanced data and high accuracy. The ensemble learning methods used are Random Forest and Light Gradient Boosting Machine (LGBM). By using the Penn-Fudan Database for Pedestrian dataset which contains a imbalanced class. The use of synthetic oversampling techniques can improve the level of accuracy in minority classes. The random forest algorithm experienced an increase in accuracy by 37% while the LGBM algorithm increased by 41%.
Co-Authors Abdul Syukur Ahmad Zainul Fanani Al zami, Farrikh Althoff, Mohammad Noval Ardytha Luthfiarta Arie Nugroho, Arie Assaqty, Mohammad Iqbal Saryuddin Ayu Pertiwi Azhar, Saifulloh Basiron, Halizah Cahyadi, Dede Faiz Dliyauddin, Muhammad Erwin Yudi Hidayat Etika Kartikadarma Fadlurrahman, Hanan Nadhif Firdaus, Iqbal Guruh Fajar Shidik Hafidhoh, Nisa'ul Hardiansyah, Raindy Wicaksana Ismail, Abdul Rahman Maknun, Lu'luul Marjuni, Aris Moch Arief Soeleman, Moch Arief Muhammad Yunan, Muhammad Nurhindarto, Aris Patue, Abdulatif Prasetya, Rudy Eko Pujiono Pujiono Ricardus Anggi Pramunendar Sari Ayu Wulandari Satya, Mohammad Wahyu Bagus Dwi Sidik, Guruh Fajar Soeleman, M. Arief Soeleman, Mochamad Arief Sukamto, Titien S. Yulianto, Satria Pradana Rizki