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All Journal Jurnal Informatika Jurnal Sarjana Teknik Informatika Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Telematika : Jurnal Informatika dan Teknologi Informasi International Journal of Advances in Intelligent Informatics Knowledge Engineering and Data Science ILKOM Jurnal Ilmiah WIDYA LAKSANA SABDAMAS Science in Information Technology Letters International Journal of Robotics and Control Systems Applied Engineering and Technology Bulletin of Social Informatics Theory and Application Journal of Artificial Intelligence in Architecture
Andri Pranolo
Universitas Ahmad Dahlan
Humanities Automotive Engineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Public Health Social Sciences Other

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Prediksi Laju Pertumbuhan Penduduk Di Kabupaten Sleman Dengan Metode Adaptive Neuro-Fuzzy Inference System (Anfis) Dan Metode Sugeno Agus Dianto; Andri Pranolo
Jurnal Sarjana Teknik Informatika Vol 6, No 3 (2018): Oktober
Publisher : Teknik Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v6i3.15248

Abstract

Pemerintah kabupaten Sleman hanya mendapatkan data penduduk di Kabupaten Sleman dilakukan hanya saat sensus penduduk saja, dimana ketika pemilu dan program pemerintah saja. Dalam sistem prediksi laju pertumbuhan penduduk menggunakan metodologi adaptive neurofuzzy infrence system (anfis) dan metode sugeno. Metode adaptive neuro-fuzzy infrence system (anfis) dimulai dengan tahap menentukan lapisan 1, lapisan 2, lapisan 3, lapisan 4, lapisan 5. tahap perancangan sistem, tahap implementasi/coding, dan tahap pengujian sistem. Sistem diuji dengan 2 metode yaitu Black Box Test dan Alpha Test. Hasil penelitian ini menghasilkan sistem prediksi Laju penduduk. Hasil perhitunan anfis untuk mengetahui perbandingan data sensus dan data hasil hitung anfis dan Hasil prediksi pada priode selanjutnya yang dihitung menggunakan metode sugeno dan metode geometri. Data tersebut menghasilkan perbandingan data sensus dan data hasil hitung anfis sebesar 0,44%, dengan hasil pengujian prediksi metode sugeno naik sebesar 16,10% pada tahun 2020 dapat diketahui sangat meningkat dan hasil pengujian dengan metode geometri sebesar 1,65% dapat diketahui laju pertumbuhan penduduk setiap tahunya. Dan kesimpulan dapat diambil perbandingan hasil sensus dengan hasil hitung anfis meningkat, sedangkan mengunakan metode sugeno lebih baik untuk memprediksi laju pertumbuhan penduduk dan dengan metodemgeometri dapat diketahui prediksi laju pertumbuhan setiap tahunnya.
KLASIFIKASI POTENSI ZAKAT DI LAZISMU DIY MENGGUNAKAN METODE K-NEAREST NEIGHBOR (K-NN) BERBASIS WEB FRAMEWORK Tedy Setyadi; Andri Pranolo; Prayitno Prayitno
Jurnal Sarjana Teknik Informatika Vol 5, No 3 (2017): Oktober
Publisher : Teknik Informatika, Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/jstie.v5i3.12370

Abstract

Zakat berperan untuk mencapai keadilan sosial ekonomi antara orang kaya dan miskin. Saat ini terdapat Lembaga Amil Zakat (LAZ) yang berperan penting dalam pengelolaan dana Zakat, Infaq, dan Shodaqah. Namun ada beberapa faktor kekurangan dalam hal penyaluran dana zakat yaitu siapa yang berhak menerima dana zakat dengan tepat sasaran dan cabang-cabang mana saja yang berpotensi mandiri dalam pengelolaan zakat. Klasifikasi dapat digunakan untuk menilai ketepatan penyaluran zakat dan mengetahui kemandirian tiap-tiap cabang LAZ berdasarkan data-data masa lalu. Data tersebut bisa digunakan untuk menerapkan metode K-NN sehingga dapat mengklasifikasi dana zakat menurut kelasnya.             Penelitian ini dilakukan untuk mengkaji tentang algoritma K-NN dan mengimplementasikan Algoritma K-NN dalam klasifikasi data. Data yang digunakan adalah data penyaluran dana zakat di Lazismu DIY dari tahun 2013 sampai 2015.Data penyaluran zakat dari cabang-cabang LAZ yang telah melalui proses cleaning data, integration data, selection data, transformation data,dananalisis diproses menggunakan metodeK-Nearest Neighbor (K-NN)  untuk  mengklasifikasikan cabang-cabang yang berpotensi membantu perekonomian daerah (mandiri) dan penyaluran dana zakat yang tepat sasaran berdasarkan  tingkat  kemiripan sejumlah nilai  variabel  k. Proses algoritma K-NN di buatmenghasilkanpattern evaluationdandisajikanmelalui knowledge presentationdenganbantuan web framework.             Hasil pengujian dilakukan terhadap 14 cabang Lazismu di DIY menghasilkan tidakada cabang di kelas Super Mandiri, 6 cabang berada dikelas Mandiri, tidakada cabang berada dikelas Cukup Mandiri, dan 8 cabang berada pada kelas Kurang Mandiri. Hasil confusion matrix dengan perbandingan 80:20 dari data uji dan data testing menghasilkan nilai accuracy sebesar 85% dan error-rate sebesar25%. Hasil accuracy>= 85% dikatakan baik dalam klasifikasi tersebut membuktikan bahwa faktor-faktor nilai atribut yang dipilih mendekati nilai significant
PEMANFAATAN AGEN CERDAS UNTUK MENGUKUR PRESTASI KERJA PEGAWAI NEGERI SIPIL (PNS) (THE USE OF INTELLIGENT AGENS FOR MEASURE THE WORKING PERFOMANCE OF CIVIL SERVANTS) Andri Pranolo
Telematika Vol 11, No 1 (2014): Edisi Juli 2014
Publisher : Jurusan Teknik Informatika

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31315/telematika.v11i1.512

Abstract

To implement the governance tasks, professional, responsible, honest, and fair civil servants are needed. It can be attained through a training which is implemented based on the system of working performance and career that focuses on the system of working performance. Therefore, the appraisal of working performance is carried out to evaluate the performance of civil servants for providing guidance to the officials in the evaluation of  unit and organization performance. This study aims to make intelligent agens design, so that the working units and governmental organizations can do evaluation based on the self-evaluation of their employees’ working performance. It refers to the government regulation number 46 year 2011, that the appraisal of the civil servants’ working performance consists of Employee’s Work Goal (EWG) and Work Behavior (WB) with the percentages for each are 60% and 40%. The intelligent agens that can be formed from this case consist of 1) Agen-evaluator who provides the feed back of working performance progress, 2) Agen-work planner who contributes in providing recommendation of jobs which are apropriate with the civil servants whose working performance is still low, and 3) Agen-record-of-performance who contribute in recording the performance of Civil Servants.
Modification of a gray-level dynamic range based on a number of binary bit representation for image compression Arief Bramanto Wicaksono Putra; Supriadi Supriadi; Aji Prasetya Wibawa; Andri Pranolo; Achmad Fanany Onnilita Gaffar
Science in Information Technology Letters Vol 1, No 1: May 2020
Publisher : Association for Scientific Computing Electronics and Engineering (ASCEE)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/sitech.v1i1.17

Abstract

The unique features of an image can be obtained by changing the gray level by modifying the dynamic range of the gray level. The gray-level dynamic range modification technique is one technique to minimize the selected features.  Bit rate reduction uses coding information with fewer bits than the original image (image compression). This study using the dynamic level of the gray level of a modified image with the concept of binary bit representation or also called bit manipulation.  Using some binary bit representation options used: 4, 5, 6, and 7 of bit can obtain the best compression performance. Measurement of compression ratio and decompression error ratio to a benchmark comparison called compression performance, which is the ultimate achievement of this study. The results of this study show the use of 6-bit binary representation has the best performance, and the resulting image compression does not resize the resolution of the original image only visually looks different.
Optimized Three Deep Learning Models Based-PSO Hyperparameters for Beijing PM2.5 Prediction Andri Pranolo; Yingchi Mao; Aji Prasetya Wibawa; Agung Bella Putra Utama; Felix Andika Dwiyanto
Knowledge Engineering and Data Science Vol 5, No 1 (2022)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v5i12022p53-66

Abstract

Deep learning is a machine learning approach that produces excellent performance in various applications, including natural language processing, image identification, and forecasting. Deep learning network performance depends on the hyperparameter settings. This research attempts to optimize the deep learning architecture of Long short term memory (LSTM), Convolutional neural network (CNN), and Multilayer perceptron (MLP) for forecasting tasks using Particle swarm optimization (PSO), a swarm intelligence-based metaheuristic optimization methodology: Proposed M-1 (PSO-LSTM), M-2 (PSO-CNN), and M-3 (PSO-MLP). Beijing PM2.5 datasets was analyzed to measure the performance of the proposed models. PM2.5 as a target variable was affected by dew point, pressure, temperature, cumulated wind speed, hours of snow, and hours of rain. The deep learning network inputs consist of three different scenarios: daily, weekly, and monthly. The results show that the proposed M-1 with three hidden layers produces the best results of RMSE and MAPE compared to the proposed M-2, M-3, and all the baselines. A recommendation for air pollution management could be generated by using these optimized models.
IDSX-Attention: Intrusion detection system (IDS) based hybrid MADE-SDAE and LSTM-Attention mechanism Hanafi Hanafi; Andri Pranolo; Yingchi Mao; Taqwa Hariguna; Leonel Hernandez; Nanang Fitriana Kurniawan
International Journal of Advances in Intelligent Informatics Vol 9, No 1 (2023): March 2023
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/ijain.v9i1.942

Abstract

An Intrusion Detection System (IDS) is essential for automatically monitoring cyber-attack activity. Adopting machine learning to develop automatic cyber attack detection has become an important research topic in the last decade. Deep learning is a popular machine learning algorithm recently applied in IDS applications. The adoption of complex layer algorithms in the term of deep learning has been applied in the last five years to increase IDS detection effectiveness. Unfortunately, most deep learning models generate a large number of false negatives, leading to dominant mistake detection that can affect the performance of IDS applications. This paper aims to integrate a statistical model to remove outliers in pre-processing, SDAE, responsible for reducing data dimensionality, and LSTM-Attention, responsible for producing attack classification tasks. The model was implemented into the NSL-KDD dataset and evaluated using Accuracy, F1, Recall, and Confusion metrics measures. The results showed that the proposed IDSX-Attention outperformed the baseline model, SDAE, LSTM, PCA-LSTM, and Mutual Information (MI)-LSTM, achieving more than a 2% improvement on average. This study demonstrates the potential of the proposed IDSX-Attention, particularly as a deep learning approach, in enhancing the effectiveness of IDS and addressing the challenges in cyber threat detection. It highlights the importance of integrating statistical models, deep learning, and dimensionality reduction mechanisms to improve IDS detection. Further research can explore the integration of other deep learning algorithms and datasets to validate the proposed model's effectiveness and improve the performance of IDS.
Application of Artificial Intelligence in Digital Architecture to Identify Traditional Javanese Buildings Sri Winiarti; Heri Pramono; Andri Pranolo
Journal of Artificial Intelligence in Architecture Vol. 1 No. 1 (2022): Artificial Intelligence for Enhancing Architectural Design
Publisher : Universitas Atma Jaya Yogyakarta

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24002/jarina.v1i1.4916

Abstract

Traditional buildings have a cultural philosophy and characterize the culture of an area. The occurrence of environmental changes, population growth, and the growth of modern buildings impact traditional buildings. Therefore, preserving those traditional buildings is needed to avoid extinction and make as cultural assets. The research aims to develop an application to help architects quantitatively measure the content of traditional architectural styles in their designs. This study uses the Artificial Intelligence (AI) method to identify buildings' similarities, acquiring traditional building data in roofs and ornaments images as a dataset totaling 650 images of roofs and 7,180 ornaments. Data processing was carried out by making architectural models, training, testing accuracy, and creating application interfaces. The algorithm used to identify similarities between building types was the Convolutional Naural Network (CNN) and the Support Vector Machine (SVM). The results of the accuracy-test using the Confusion matrix method reached an accuracy value of 99.5% in identifying building similarities and 85% in classifying building types.
Nondestructive Chicken Egg Fertility Detection Using CNN-Transfer Learning Algorithms Shoffan Saifullah; Rafal Drezewski; Anton Yudhana; Andri Pranolo; Wilis Kaswijanti; Andiko Putro Suryotomo; Seno Aji Putra; Alin Khaliduzzaman; Anton Satria Prabuwono; Nathalie Japkowicz
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol 9, No 3 (2023): September
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v9i3.26722

Abstract

This study explores the application of CNN-Transfer Learning for nondestructive chicken egg fertility detection. Four models, VGG16, ResNet50, InceptionNet, and MobileNet, were trained and evaluated on a dataset using augmented images. The training results demonstrated that all models achieved high accuracy, indicating their ability to accurately learn and classify chicken eggs’ fertility state. However, when evaluated on the testing set, variations in accuracy and performance were observed. VGG16 achieved a high accuracy of 0.9803 on the testing set but had challenges in accurately detecting fertile eggs, as indicated by a NaN sensitivity value. ResNet50 also achieved an accuracy of 0.98 but struggled to identify fertile and non-fertile eggs, as suggested by NaN values for sensitivity and specificity. However, InceptionNet demonstrated excellent performance, with an accuracy of 0.9804, a sensitivity of 1 for detecting fertile eggs, and a specificity of 0.9615 for identifying non-fertile eggs. MobileNet achieved an accuracy of 0.9804 on the testing set; however, it faced challenges in accurately classifying the fertility status of chicken eggs, as indicated by NaN values for both sensitivity and specificity. While the models showed promise during training, variations in accuracy and performance were observed during testing. InceptionNet exhibited the best overall performance, accurately classifying fertile and non-fertile eggs. Further optimization and fine-tuning of the models are necessary to address the limitations in accurately detecting fertile and non-fertile eggs. This study highlights the potential of CNN-Transfer Learning for nondestructive fertility detection and emphasizes the need for further research to enhance the models’ capabilities and ensure accurate classification.
Economic support online-technology: a comparison between China and Indonesia Pranolo, Andri; Nuryana, Zalik; Sularso, Sularso; Fadillah, Dani
Bulletin of Social Informatics Theory and Application Vol. 7 No. 1 (2023)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v7i1.486

Abstract

Following the United States, China is the world's second-biggest economy and the world's largest exporter of goods. Chinese and Indonesian economies differ significantly in terms of development and implementation and the availability of technology and transportation to support them, particularly in terms of GDP value and other indicators of economic health. The topography and climate of China and Indonesia are covered in detail in this study, as is the economic relationship between China and Indonesia, covered in the second section. According to the economic viewpoints, the economic topic incorporates technology and transportation. Both countries must improve their cooperation to reap mutual benefits in the future.
Pelatihan Pengembangan Bisnis Mahasiswa Asing di Tiongkok Hariyanti, Nunik; Pranolo, Andri; Salim, Agus; Fadillah, Dani; Khotimah, Husnul; Firdaus, Nalendra
SABDAMAS Vol 2 No 1 (2023): SABDAMAS
Publisher : Lembaga Penelitian dan Pengabdian kepada Masyarakat Unika Atma Jaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25170/sabdamas.v2i1.5019

Abstract

Tiongkok merupakan salah satu pusat negara tujuan untuk melanjutkan studi. Salah satu kota tujuan studi adalah kota Nanjing. Bisnis ekspor-impor merupakan peluang yang dapat dilakukan oleh para mahasiswa untuk meningkatkan penghasilan tambahan bagi mahasiswa. Selama ini mahasiswa hanya berbisnis dalam skala kecil, tetapi belum menjangkau pasar antarnegara. Tidak jarang mahasiswa kesulitan menemukan cara untuk memulai bisnis ekspor-impor ini. Tujuan pengabdian ini adalah memberikan pelatihan pengembangan bisnis ekspor-impor bagi mahasiswa asing di Tiongkok melalui kegiatan pelatihan penentuan produk ekspor-impor, pelatihan mempersiapkan dokumen ekspor-impor, dan pengenalan membangun bisnis ekspor-impor. Kegiatan ini dilaksanakan secara luring di Tiongkok dengan metode presentasi, diskusi, dan pendampingan yang dilakukan sejak Juli hingga September 2023. Pelatihan meliputi kegiatan penentuan produk ekspor-impor, pelatihan mengenal dokumen ekspor-impor dan pembuatan perusahaan ekspor-impor. Hasil pretest dan post-test kegiatan ini menunjukkan bahwa pengetahuan peserta meningkat dari rata-rata 7,2 menjadi 9,7 terkait dengan ekspor-impor.
Co-Authors ., Suparman AA Sudharmawan, AA Abdalla, Modawy Adam Ali Achmad Fanany Onnilita Gaffar Adhi Prahara Adhi Prahara Adhi Susanto Afief Akmal Afiqa, Nurul Agung Bella Putra Utama Agus Dianto Agus Salim Aji Prasetya Wibawa Akbari, Ade Kurnia Ganesh Albas, Juan Alin Khaliduzzaman Andiko Putro Suryotomo Anton Satria Prabuwono Anton Yudhana Azhari, Ahmad Azlan, Faris Farhan Ba, Abdoul Fatakhou Bambang Widi Pratolo Camargo, Jair Dani Fadillah Elhindi, Mohamed Fachrul Kurniawan Fadhilla, Akhmad Fanny Felix Andika Dwiyanto Firdaus, Nalendra Firdaus, Nalendra Putra Ghazali, Ahmad Badaruddin Hanafi Hanafi Hariyanti, Nunik Heni Pujiastuti Heri Pramono Hoz, César De La Ismail, Amelia Ritahani Khadir, Mohammed Tarek Leonel Hernandez Leonel Hernandez, Leonel Mao, Yingchi Mirghani, Abdelhameed Mokhtar, Nur Azizah Mohammad Muhammad, Abdullahi Uwaisu Nanang Fitriana Kurniawan Nathalie Japkowicz Nisa, Syed Qamrun Noormaizan, Khairul Akmal Nor Amalina Abdul Rahim Nuril Anwar Nuryana, Zalik Omar, Abdalwahab Omer, Abduelrahman Adam Onie Yudho Sundoro Paramarta, Andien Khansa’a Iffat Prayitno Prayitno Rafal Drezewski Rafał Dreżewski Roman Voliansky Saifullah, Shoffan Sarina Sulaiman Sarina Sulaiman Seno Aji Putra Setyaputri, Faradini Usha Snani, Aissa Sri Winiarti Sularso Sularso, Sularso Suparman Supriadi Supriadi Taqwa Hariguna Tedy Setyadi Triono, Alfiansyah Putra Pertama Uriu, Wako Utama, Agung Bella Putra Wilis Kaswijanti Yingchi Mao Yingchi Mao Yingchi Mao Yingchi Mao Zhou, Xiaofeng