Lab-IS/filkom-research-papers huggingface dataset:
Database optimization for improved system performance and response time of hospital management information system
Authors: Bayu Rahayudi, Nurizal Dwi Priandani, Buce Trias Hanggara, Wayan Firdaus Mahmudy
Journal: Bulletin of Social Informatics Theory and Application
Year: 2021
Topics: Database Systems; System Optimization
Abstract: A Regional Hospital in East Java has implemented a Hospital Management Information System, namely SIMRS, in their data management system but has experienced problems in the form of slow system response when accessed by many users, was experienced in the last years when the system had been running for four years since 2016. The system’s slow response causes hospital services to be disrupted and also the quality of service to decline. So, an analysis to the existing database system is carried out, which includes an analysis of the system‘s database performance. Since many SIMRS use database servers on their data processing, then their applications will be based on executing queries and stored procedures (most of the queries are stored in stored procedures). So that, analysis of those queries will be carried out. The optimization process will include analyzing and mapping the database’s queries, profiling, and analyzing the Actual Execution Plan. By doing so, it is known which parts of the query are causing a decrease in performance and time system response. Based on the analysis results, recommendations are given for improving and rewriting several stored procedures and query statements, and the system response time is getting better.
Performance Comparison of Genetic Algorithm and Particle Swarm Optimization in Solving Product Storage Optimization
Authors: Nindynar Rikatsih, Mochammad Anshori, Wayan Firdaus Mahmudy
Year: 2019
Topics: Optimization Algorithms; Genetic Algorithm; Particle Swarm Optimization
Abstract: Product storage provides considerable influence in obtaining profit for traders in selling products. However, the existing product storage is inefficient because products with high selling prices are stored in large quantities even though this does not necessarily give a high profit because it can also provide high losses. Traders must be able to determine the number of products stored at high selling prices and smaller losses. We propose Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) as an optimization method. We use GA with real code representation, one cut point crossover, insertion mutation and elitism selection. We also use PSO to solve the same problem. Both GA and PSO have been proved that can solve optimization problem. We compare which performance of them is better based on profit gained, fitness value and computational time. The experiment result shows that with the same problem …
Optimasi Bobot Awal Extreme Learning Machine menggunakan Algoritme Genetika untuk Klasifikasi Penanganan Human Papilloma Virus
Authors: Rizki Ramadhan, Wayan Firdaus Mahmudy
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Machine Learning; Genetic Algorithm
Abstract: Human Papilloma Virus adalah virus penyebab penyakit kutil pada manusia. Banyak sekali jenis penanganan virus ini, jenis penanganan yang umum adalah immunotherapy dan cryotherapy. Parameter gejala-gejala yang muncul pada penderita hampir mirip sehingga diperlukan penentuan penanganan secara tepat. Didasarkan hal tersebut, maka pada penelitian ini digunakan metode Extreme Learning Machine untuk mengklasifikasi jenis penanganan Human Papilloma Virus. Parameter gejala yang digunakan terdapat 6 parameter dan kelas yang digunakan adalah immunotherapy dan cryotherapy. Dalam penelitian ini, bobot awal pada Extreme Learning Machine dioptimasi oleh Algoritme Genetika kemudian bobot tersebut digunakan oleh metode Extreme Learning Machine untuk proses klasifikasi jenis penanganan Human Papilloma Virus. Jumlah data yang digunakan sebanyak 118 data dengan rasio data untuk proses latih dan uji adalah 80: 20. Parameter Extreme Learning Machine yang digunakan adalah hidden neuron berjumlah 10 node serta fungsi aktivasi yang dipakai adalah sigmoid biner. Hasil pengujian memperoleh tingkat akurasi terbaik klasifikasi sebesar 100% untuk kelas cryotherapy dan 100% untuk kelas immunotherapy pada 3 dari 10 kali percobaan dengan rata-rata waktu komputasi selama 350, 3 detik menggunakan bobot awal yang telah dioptimasi oleh Algoritme Genetika dengan parameter terbaik ukuran populasi 70, jumlah generasi 160, crossover rate 0, 9 dan mutation rate 0, 1.
Optimasi kebutuhan gizi untuk ibu hamil dengan menggunakan hybrid algoritma genetika dan simulated annealing
Authors: Binti Robiyatul Musanah, Wayan Firdaus Mahmudy, Indriati Indriati
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Optimization Algorithms; Genetic Algorithm; Simulated Annealing
Abstract: Kebutuhan gizi sangat diperlukan bagi ibu hamil. Gizi yang baik adalah gizi yang seimbang serta sesuai dengan kebutuhan. Jika kebutuhan ibu hamil tidak sesuai bisa mengalami Kekurangan Energi Kronis (KEK). Dengan permasalahan tersebut dibutuhkan suatu sistem untuk pemenuhan gizi ibu hamil yang dapat memberikan solusi berupa komposisi bahan makanan yang sesuai dengan metode Hybrid Algoritme Genetika dan Simulated Annealing. Dalam proses penyelesaian permasalahan ini pada Simulated Annealing atau Genetic Algorithm. Proses hybrid ini pada proses crossover menggunakan one-cut point, insertion mutation untuk proses mutasi dan penggunaan elitsm pada tahap seleksi serta Simulated Annealing. Hasil pengujian yang dilakukan sebelumnya diperoleh parameter dengan metode Hybrid GA dan SA didapatkan nilai parameter-parameter terbaik yaitu sebesar 100 (ukuran populasi) fitness yang didapat rata-rata 0.06268, nilai 105 (generasi) memperoleh rata-rata fitness 0.06823, kombinasi Cáµ£ dan Máµ£ dengan nilai 0, 4 dengan 0, 6 nilai fitness rata-rata adalah 0.06792, serta temperatur awal dengan nilai 1 serta alpha yaitu 0, 5 didapatkan fitness terbaik dengan rata-rata sebesar 0.06800 serta 0.08876. Hasil fitness hibridisasi GA dan SA mencapai 0, 08804 lebih tinggi dari pada nilai rata-rata fitness GA sebesar 0, 05519 dan fitness SA yaitu 0, 04382 dengan waktu yang ditentukan adalah 1 menit dan kebutuhan yang dihasilkan dari sistem kurang mencukupi kebutuhan gizi ibu hamil.
Integrating fuzzy logic and genetic algorithm for upwelling prediction in Maninjau Lake
Authors: Muhammad Rofiq, Yogie Susdyastama Putra, Wayan Firdaus Mahmudy, Herman Tolle, Ida Wahyuni, Philip Faster Eka Adipraja, Hafrijal Syandri
Journal: TELKOMNIKA (Telecommunication Computing Electronics and Control)
Year: 2019
Topics: Fuzzy Logic; Genetic Algorithm
Abstract: Upwelling is a natural phenomenon related with the increase in water mass that also occurs in Maninjau Lake, West Sumatra. The upwelling phenomenon resulted in considerable losses for freshwater fish farming because make mass mortalities of fish in farming using the method of floating net cages (karamba jaring apung/KJA). It takes a system that can predict the possibility of upwelling as an early warning to the community, especially fish farming to immediately prepare early anticipation of upwelling prevention. With historical water quality monitoring data at six sites in Maninjau Lake for 17 years, a prediction model can be made. There are three input criteria for Tsukamoto FIS that is water temperature, pH, and dissolve oxygen (DO). The model is built with fuzzy logic integration with the genetic algorithm to optimize the membership function boundaries of input and output criteria. After the optimization, hybrid Tsukamoto FIS and genetic algorithm successfully make a correct upwelling prediction on of 16 data with 94% accuracy.
Penerapan Metode Decision Tree dan Algoritme Genetika Untuk Klasifikasi Risiko Hipertensi
Authors: Selly Kurnia Sari, Wayan Firdaus Mahmudy
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Machine Learning; Decision Tree; Genetic Algorithm
Abstract: Hipertensi ini merupakan salah satu penyakit paling mematikan ketiga setelah stroke dan tuberkulosis yaitu mencapai 6, 7% dari keseluruhan umur di Indonesia. Hal ini menunjukkan bahwa penyakit hipertensi di Indonesia membutuhkan perhatian yang serius sebagai upaya dalam menangani masalah ini. Penanganan sangat dibutuhkan sebagai langkah deteksi dini terhadap penyakit hipertensi. Dalam kaitannya dengan klasifikasi untuk deteksi penyakit hipertensi, metode yang dapat digunakan salah satunya adalah Decision Tree (DT). Namun pada penelitian sebelumnya metode DT menghasilkan akurasi yang terbilang rendah. Untuk mengoptimalkan tingkat akurasi metode DT, maka digunakan Genetic Algorithm (GA). GA digunakan untuk menghasilkan aturan baru terhadap DT. Untuk mengetahui perbedaan hasil akurasi, maka dilakukan pengujian perbandingan DT-C4. 5 dan DT-GA. Pengujian tersebut menggunakan data uji yang sama. Hasil pengujian menunjukkan algoritme DT-GA menghasilkan rata-rata akurasi tertinggi 84%, sedangkan algoritme DT-C4. 5 menghasilkan rata-rata akurasi tertinggi 70, 5%. Parameter-parameter terbaik menghasilkan akurasi terbaik adalah ukuran populasi 60, Cr= 0, 3, Mr= 0, 7 dengan jumlah generasi maksimal yang digunakan adalah 10. Dari hasil pengujian tersebut dapat disimpulkan bahwa GA dapat digunakan untuk menghasilkan aturan baru dari DT.
High Dimensional Data Clustering using Self-Organized Map.
Authors: Ruth Ema Febrita, Wayan Firdaus Mahmudy, Aji Prasetya Wibawa
Journal: Knowl. Eng. Data Sci.
Year: 2019
Topics: Clustering; Self-Organized Map
Abstract: Houses are promising investment commodity in the last decade. The house price index in Indonesia is also experienced inflation, as well as the level of sales of home property [1]. The price is influenced by several factors such as the interest rate, inflation on house ownership loans, inflation in building materials prices, and inflation in workers' minimum wages. There are many different types of houses offered along with various features, which sometimes make prospective buyers confused to determine their choice. Another three factors that influence house pricing are physical attributes, accessibility, and developer reputation [2]. Physical attributes refer to house attributes that are visible and measurable, such as the land area, building area, number of rooms, number of bathrooms, and the availability of the living room. The accessibility refers to the house location that determines the ease of access to public facilities, such as hospitals, schools, campuses, markets, etc. Commonly, the closer location of a house with many public facilities may cause the more expensive of the house price. Some other economic phenomena that can affect the house prices are the interest rate, inflation and the Gross Domestic Products (GDP)[3].
New Insight in Cervical Cancer Diagnosis Using Convolution Neural Network Architecture
Authors: Ach Khozaimi, Wayan Firdaus Mahmudy
Journal: arXiv preprint arXiv:2410.17735
Year: 2024
Topics: Deep Learning; Convolutional Neural Network
Abstract: The Pap smear is a screening method for early cervical cancer diagnosis. The selection of the right optimizer in the convolutional neural network (CNN) model is key to the success of the CNN in image classification, including the classification of cervical cancer Pap smear images. In this study, stochastic gradient descent (SGD), RMSprop, Adam, AdaGrad, AdaDelta, Adamax, and Nadam optimizers were used to classify cervical cancer Pap smear images from the SipakMed dataset. Resnet-18, Resnet-34, and VGG-16 are the CNN architectures used in this study, and each architecture uses a transfer-learning model. Based on the test results, we conclude that the transfer learning model performs better on all CNNs and optimization techniques and that in the transfer learning model, the optimization has little influence on the training of the model. Adamax, with accuracy values of 72.8% and 66.8%, had the best accuracy for the VGG-16 and Resnet-18 architectures, respectively. Resnet-34 had 54.0%. This is 0.034% lower than Nadam. Overall, Adamax is a suitable optimizer for CNN in cervical cancer classification on Resnet-18, Resnet-34, and VGG-16 architectures. This study provides new insights into the configuration of CNN models for Pap smear image analysis.
Automatic selection kernel with ensemble concept in support vector machine (SVM) for classification of soybean plant disease
Authors: Fendy Yulianto, Erfan Nur Kholis Efendi, Wayan Firdaus Mahmudy
Year: 2022
Topics: Machine Learning; Support Vector Machine
Abstract: Soybean is one of the plants with high vegetable protein value as a substitute for animal protein. In the process of cultivating soybeans, soybeans are often exposed to diseases caused by pathogens caused by viruses, bacteria and pests. To find out the disease usually requires direct observation to the field which requires an expert, so we need a way to be able to identify the disease automatically without having to directly monitor the field with an expert. SVM method is one method that can be used to classify the types of diseases that attack soybean plants. The SVM method has a lot of Kernel functions that can be used, where the Kernel is the core of the SVM method process, there are many kernels that can be used so that if you choose the wrong Kernel will have an impact on the results obtained. To overcome this problem, this research applies 3 Ensemble Kernel concepts which are used to combine several …
Performance of Information Gain and PCA Feature Selection for Determining Ripen Susu Banana Fruits
Authors: Candra Dewi, Endang Arisoesilaningsih, Wayan Firdaus Mahmudy
Year: 2022
Topics: Feature Selection; Principal Component Analysis
Abstract: Susu banana fruits has a uniqueness, where is the difference of slightly ripe and ripe susu banana at the ripen stage is perfectly difficult to distinguish visually because both have almost the same yellow color. Therefore, this study performed identification using a fruit image-based computer vision to replace the human visual. The almost similar characteristics of susu banana at slightly ripe, ripe and riper stage were selected to get a dominant character that has a high influence. The ability of information gain (IG) and principal component analysis (PCA) and combined IG-PCA features selection was evaluated to determine the influence of correlation and probability of each feature on each class. Tests were conducted on clean-peeled and spotted peel susu banana with 3 levels of ripeness at the ripen stage to determine the impact of IG, PCA and combined IG-PCA on classification using extreme learning machines …
Klasifikasi Risiko Penyakit pada Ibu Hamil menggunakan Metode Modified K-Nearest Neighbor (MKNN)
Authors: Yogi Pinanda, Wayan Firdaus Mahmudy, Edy Santoso
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2022
Topics: Machine Learning; K-Nearest Neighbor
Abstract: Ibu hamil perlu menambah pengetahuan untuk mengetahui seberapa besar tingkat risiko terkena penyakit, tertentu dikarenakan rentannya kondisi pada ibu hamil. Klasifikasi tingkat risiko penyakit pada ibu hamil diharapkan dapat membantu pengguna dalam menemukan solusi yang tepat untuk menanggulanginya. Metode klasifikasi yang digunakan untuk mengetahui tingkat risiko penyakit ibu hamil ini menggunakan Modified K-Nearest Neighbor (MKNN). Pengklasifikasian tingkat risiko penyakit pada ibu hamil menggunakan metode Modified K-Nearest Neighbor (MKNN) dapat mempermudah untuk mendeteksi penyakit berdasarkan faktor-faktor yang ada. Metode Modified K-Nearest Neighbor (MKNN) diimplementasikan pada mesin inferensi sistem pakar agar dapat dilakukannya penarikan kesimpulan berdasarkan pengetahuan yang ada. Hasil akurasi sistem yang didapatkan setelah melakukan pengujian yaitu sebesar 85% yang menunjukkan bahwa metode Modified K-Nearest Neighbor (MKNN) cocok untuk studi tingkat risiko penyakit pada ibu hamil.
Website visitors forecasting using recurrent neural network method
Authors: Putu Bagus Arya, Wayan Firdaus Mahmudy, Achmad Basuki
Journal: Journal of Information Technology and Computer Science
Year: 2021
Topics: Deep Learning; Recurrent Neural Network
Abstract: The number of visitors and content accessed by users on a site shows the performance of the site. Therefore, forecasting needs to be done to find out how many users a website will come. This study applies the Long Short Term Memory method which is a development of the Recurrent Neural Network method. Long Short Term Memory has the advantage that there is an architecture of remembering and forgetting the output to be processed back into the input. In addition, the ability of another Long Short Term Memory is to be able to maintain errors that occur when doing backpropagation so that it does not allow errors to increase. The comparison method used in this study is Backpropagation. Neural Network method that is often used in various fields. The testing using new visitor data and first time visitors from 2018 to 2019 with vulnerable time per month. The computational experiment prove that the Long Short Term Memory produces better result in term of the mean square error (MSE) comparable to those achieved by Backpropagation Neural Network method.
Detection of hepatitis B virus-associated hepatocellular carcinoma disease using hybrid hierarchical k-means clustering and SVM algorithm
Authors: Lailil Muflikhah, Nashi Widodo, Wayan F Mahmudy, Solimun
Journal: Current Bioinformatics
Year: 2021
Topics: Machine Learning; Clustering; Support Vector Machine
Abstract: Background Hepatocellular carcinoma (HCC) is a serious disease and is the third main cause of death in the world. Hepatitis B virus infection can lead to HCC. The virus introduces genetic material into the host, damages DNA, and interferes with the activity of the apoptotic and tumor suppressors to trigger the formation of an oncogene. However, most of these cases are discovered after cancer enters stage three or four. Objective Early detection of HCC through machine learning algorithm approach using data set: DNA sequence of HBx HepB virus. Methods The research method used is the development of a Support Vector Machine classifier algorithm for carcinoma detection. The large data volume and unbalance data distribution in class can decrease the accuracy rate and sensitivity. Therefore, this paper proposed a hybrid of Hierarchical k-Means clustering and SVM algorithms to detect HCC disease using …
Application of Density Based Spatial Clustering Application With Noise (DBSCAN) in Determining the Quality of Keprok Orange and Siam Orange Hybrid in the Research Center of …
Authors: Faiz Alqorni, Wayan Firdaus Mahmudy, Agus Wahyu Widodo
Journal: JITeCS (Journal of Information Technology and Computer Science)
Year: 2021
Topics: Machine Learning; Clustering Algorithms
Abstract: One of the tasks of the Indonesian Citrus and Subtropical Research Institute is research on crossing between citrus varieties to produce saplings with the best quality products through observation of the fruit produced. Because the amount of fruit production studied is very large, it requires a fast and accurate observation process, one of which is the clustering method of data mining. Observations were made using a clustering process or grouping Density Based Spatial Clustering Application with Noise (DBSCAN) on fruit characteristics that indicate quality. DBSCAN works by grouping data based on density, so that it is expected to find several data groups that are close to each other which shows the tendency of the quality of the observed fruit data as well as labeling outlays for data that are too far from the crowd. The results of the grouping will be analyzed to find out the number and characteristics of the groups formed where the results of the grouping are assessed using the Silhouette Coefficient method to determine the best parameter values. The results obtained in this study are obtained three group results which will be divided into medium quality, good, and not so good. The quality of grouping using the Silhouette Coefficient value of 0.69.
Rainfall Prediction in Tengger Indonesia: A System Dynamics Approach
Authors: Ida Wahyuni, Philip Faster Eka Adipraja, Wayan Firdaus Mahmudy, Atiek Iriany
Year: 2021
Topics: System Dynamics; Environmental Modeling
Abstract: Commonly, rainfall prediction uses historical data or the time series data. But there are many factors can affect rainfall such as temperature and humidity. Therefore, an approach is needed that can model forecasting rainfall not only uses time series data but also includes other supporting variables. System dynamics is an approach for modeling and simulation of the data. By modeling the rainfall data from the previous year which includes temperature and humidity data using system dynamics, prediction of rainfall can be simulated. The prediction results using systems dynamics approach generate root mean square error (RMSE) smaller than previous studies. The result of RMSE with system dynamics for Puspo area is 6.767. It is smaller than the RMSE results with a hybrid Tsukamoto FIS with genetic algorithm method which is 7.30.
Food Menu Recommendations Based on Recommended Dietary Allowances using Genetic Algorithm
Authors: Muhammad Ardhian Megatama, Wayan Firdaus Mahmudy, Edy Santoso
Journal: Bulletin of Culinary Art and Hospitality
Year: 2021
Topics: Genetic Algorithms; Nutrition Science
Abstract: Nowadays, there are still often unbalanced nutritional problems such as overnutrition or malnutrition. Many factors can affect it, one of which is an unbalanced diet. One solution that can be done is a system for optimizing nutritional needs. In this study, the method used for optimization is genetic algorithms. Genetic algorithms are one of the metaheuristic methods that are often used for optimization problems. A particular chromosome representation is designed to provide suitable solutions. The system can provide food recommendations with nutrients close to a person's nutritional needs by using the genetic algorithm. Based on the test results obtained, the difference in nutrition from food recommendations with nutritional needs is below 5 percent.
Design and Implementation of Automatic Weather Station using MQTT Protocol
Authors: Ida Wahyuni, Faddli L Wibowo, Fandisya Rahman, Wayan Firdaus Mahmudy, Atiek Iriany
Journal: Cyber Physical, Computer and Automation System: A Study of New Technologies
Year: 2021
Topics: IoT; MQTT Protocol
Abstract: Indonesia is a country with a large territory with various types of weather. However, the weather station in Indonesia is still not equipped with remote data transmission method automatically. Automatic weather station (AWS) which was developed in this study, has the ability to transmit weather data from measurement automatically using technology Message Queuing Telemetry Transport (MQTT) protocol. Based on the testing process by using the MQTT protocol on AWS, the packet data required in the data transmission is quite low, therefore the bandwidth requirement becomes lower as well. The bandwidth usage can be minimized by up to 64%. Moreover, the delay time required for data transmission using MQTT only 0.003 seconds. It is shorter when compared with HTTP which needs 16,658 seconds of delay time. Because of that, the data transmission becomes faster. It is proved that using MQTT on …
Optimasi Asymmetric City Tour di Kota Kediri Menggunakan Ant Colony System
Authors: Abidatul Izzah, Benni A Nugroho, Wayan F Mahmudy, Fitra A Bachtiar
Journal: J. Nas. Tek. Elektro dan Teknol. Inf
Year: 2020
Topics: Swarm Intelligence; Ant Colony System
Abstract: Kediri City is a stopover/transit city and has many potentials in the fields of tourism, education, and industry. Thus, the City of Kediri became one of the cities that are very likely to develop and be crowded. Therefore, it is required to model city tours in several primary fields of Kediri City. In the literature, determining the optimum route can be approached as a traveling salesman problem. However, traveling salesman problem model cannot be used to determine the city tour path as the distance among the point may vary. In this study, we used the concept of asymmetric traveling salesman problem to solve the city tour path. Furthermore, we used the ant colony system algorithm to solve this problem. The cases resolved in this study are the location of the tourism center, industrial center, and education center in Kediri City. The results show the ant colony system is capable of providing optimum tour route solutions, namely the city tourist route 34.65 km, the industrial route 21.19 km, and the school route 28 km.
Classification tuberculosis dna using lda-svm
Authors: Mochammad Anshori, Wayan Firdaus Mahmudy, Ahmad Afif Supianto
Journal: Journal of Information Technology and Computer Science
Year: 2019
Topics: Machine Learning; Healthcare
Abstract: Tuberculosis is a disease caused by the mycobacterium tuberculosis virus. Tuberculosis is very dangerous and it is included in the top 10 causes of the death in the world. In its detection, errors often occur because it is similar to other diffuse lungs. The challenge is how to better detect using DNA sequence data from mycobacterium tuberculosis. Therefore, preprocessing data is necessary. Preprocessing method is used for feature extraction, it is k-Mer which is then processed again with TF-IDF. The use of dimensional reduction is needed because the data is very large. The used method is LDA. The overall result of this study is the best k value is k= 4 based on the experiment. With performance evaluation accuracy= 0.927, precision= 0.930, recall= 0.927, F score= 0.924, and MCC= 0.875 which obtained from extraction using TF-IDF and dimension reduction using LDA.
Convergence Analysis in Swarm Intelligence for City Tour Optimization
Authors: Abidatul Izzah, Benni A Nugroho, Wayan F Mahmudy, Fitra A Bachtiar, Toga A Cinderatama, Yuita A Sari
Year: 2019
Topics: Swarm Intelligence; City Planning
Abstract: Particle swarm optimization (PSO) algorithm has been widely used to solve many problems. However, PSO has limitation in dealing with premature convergence when each particle unable to move to find the global optimum solution. This research has investigated the various conditions for the PSO to determine when a premature convergence happened. We used city parks in Kediri City, Indonesia as an object for a city tour optimization. Furthermore, PSO by adding mutation operator belongs to Genetic Algorithm and dividing the swarm group into sub-swarm are used to investigate the convergence condition because they have been proven can successfully avoid a premature convergence. The result shows that the solutions produced by the addition of these operators can find better solutions compared to the simple PSO.
Optimisation of Rice Fertiliser Composition using Genetic Algorithms.
Authors: Retno Dewi Anissa, Wayan Firdaus Mahmudy, Agus Wahyu Widodo
Journal: Knowl. Eng. Data Sci.
Year: 2019
Topics: Genetic Algorithms; Agriculture
Abstract: Fertiliser is very important for rice growth and evolution. It has one or more elements, used as a replacement of element in plants. Fertilisation can be interpreted as a soil nutrient enhancer. Fertiliser is divided into several types based on where they are come from [1]; such as inorganic, organic and biological fertiliser. There are two groups of inorganic fertilisers based on the content of nutrients: single and compound fertiliser [2][3][4]. Single fertiliser only had one content. There are three kinds of single fertiliser which had the main nutrients such as nitrogen (N), phosphorus (P), potassium (K) and magnesium (Mg). Some examples of single fertilisers are nitrogen, phosphorus, potassium, and magnesium fertiliser. On the other hands, compound fertiliser is a mixture of two or more nutrients. Some examples of mixture is blended again into NP and NPK. In rice planting, the right composition of fertilizer is essentially needed. If the composition given is not correct, the growth and quality of rice produced is inadequate or the costs incurred become more wasteful and even unaffordable [5][6]. Farmers need an appropriate combination in order to obtain good results and minimum expenses of using fertiliser. The combination should consist of s nitrogen (n), phosphorus (p) and potassium (k). This composition is one of the solutions used in composing the optimal fertiliser for rice plants. Often, farmers do trial and error or follow others experience to get the right composition of fertiliser. The problem will arise if the raw material for fertilizer changes, then the farmer must change the composition that he knew beforehand. They must try harder to get the right …
Optimasi Algoritme Genetika Untuk Memaksimalkan Laba Pembangunan Perumahan
Authors: Muhammad Faris Mas' ud, Imam Cholisoddin, Wayan Firdaus Mahmudy
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2019
Topics: Genetic Algorithms; Real Estate
Abstract: Tempat tinggal ialah kebutuhan dasar. Fungsi utama tempat tinggal yaitu untuk tempat beristirahat, keamanan dan beraktifitas dengan keluarga. Permintaan pembangunan tempat tinggal dan pertumbuhan penduduk di Kota Malang yang semakin tinggi membuat semakin tinggi pula permintaan pembangunan rumah. Dalam membangun rumah, perusahaan pengembang selalu mengutamakan keuntungan di setiap pembangunannya tanpa mengurangi kualitas bangunan. Pembangunan rumah membutuhkan sumber daya manusia dan beberapa material yang terbatas, oleh karena itu Algoritme Genetika akan sangat membantu dalam segi pencarian optimasi keuntungan. Berdasarkan beberapa penelitian Algoritme Genetika lain, algoritme ini menghasilkan solusi yang diharapkan seperti: optimasi laba jilbab yang optimal, optimasi distribusi barang yang efisien dan optimasi pemilihan pekerja bangunan yang tepat sasaran. Sesuai pengujian yang dilakukan dengan menggunakan data dari Perumahan Margobasuki Residence, didapatkan jumlah keuntungan yang optimal.
Performance Comparison of Dimensional Reduction using Principal Component Analysis with Alternating Least Squares in Modified Fuzzy Possibilistic C-Means and Fuzzy …
Authors: Edi Satriyanto, Ni Wayan Surya Wardhani, Syaiful Anam, Wayan Firdaus Mahmudy
Journal: International Journal on Advanced Science, Engineering & Information Technology
Year: 2024
Topics: Dimensionality Reduction; Principal Component Analysis
Abstract: The clustering method is said to be good if it has resistance to outlier data. One cluster method resistant to outlier data is Fuzzy Possibilistic C-Means (FPCM). FPCM performance on outlier data still has the potential for overlap between cluster members in different clusters, resulting in decreased cluster quality. The Modified Fuzzy Possibilistic C-Means (MFPCM) method is used to modify FPCM in its objective function by inserting updated weight values to increase FPCM performance. In this research, improving the quality of FPCM and MFPCM clusters was carried out by reducing data dimensions through Principal Component Analysis using Alternating Least Squares (PRINCALS) so that members of each cluster do not overlap in the right cluster. The PRINCALS results of the FPCM method have better performance with silhouette values and BSS/TSS ratios of 0.4108 and 60% compared to values without …
Reliable wireless sensor network planning with multipath topology through relay placement optimization
Authors: Kasyful Amron, Wuryansari M Kusumawinahyu, Syaiful Anam, Wayan F Mahmudy
Journal: Journal of Robotics and Control (JRC)
Year: 2024
Topics: Wireless Sensor Networks; Network Planning
Abstract: Recent developments in Wireless Sensor Networks (WSN) focus on scalability and reliability. This research addresses the challenge of improving reliability in WSNs through optimal relay placement and multipath topology design. A heuristic method with a Multi-Objective Optimization (MOO) approach is proposed to solve this problem. The proposed method integrates a modified Genetic Algorithm (GA) with Particle Swarm Optimization (PSO) characteristics. The hybrid approach aims to minimize the number of relays and associated communication costs while maintaining network reliability. The method encodes relay positions and quantities into GA chromosomes that are updated by mutation, crossover, and PSO-inspired particle motion. Simulations are performed in a simplified square area with twenty randomly placed sensors, a hundred and thirty-two arranged relays, and a single sink node. As a result, the simulation generated two multipath topologies that offer unique advantages. The first emphasizes relay efficiency (61 relays, with 2096 costs), while the second ensures lower communication costs (64 relays, 1832 costs). Comparisons with alternative algorithms, including Dijkstra, A-star, GA, and PSO, prove the superiority of the proposed approach. The optimum results obtained with a composition of 95% GA and 5% PSO, outperform alternative algorithms in terms of relay efficiency and communication cost. This research contributes to the field by providing a robust solution for designing reliable multipath WSNs with a minimum number of relays.
Membangun Sistem Cerdas dengan Kecerdasan Buatan: Menjawab Tantangan Permasalahan Dunia Modern
Authors: Wayan Firdaus Mahmudy, Agus Wahyu Widodo, Arif Muntasa
Year: 2023
Topics: Artificial Intelligence; Intelligent Systems
Abstract: Buku ini membahas penerapan kecerdasan buatan (artificial intelligence, AI) dalam membangun sistem cerdas untuk menghadapi tantangan permasalahan yang kompleks di era modern. Buku ini mengeksplorasi berbagai solusi AI yang dapat digunakan untuk menjawab tantangan dunia modern yang meliputi bidang kesehatan, keuangan, lingkungan, transportasi, pertanian, peternakan, dan banyak lagi. Setiap bab dalam buku ini didedikasikan untuk membahas satu kelompok metode AI dan menghadirkan studi kasus dunia nyata yang mengilustrasikan penerapan metode tersebut beserta solusi yang dihasilkan. Dalam setiap bab, pembaca akan diperkenalkan pada metode AI yang berbeda, seperti jaringan saraf tiruan (artificial neural networks), teknik optimasi menggunakan algoritma genetika (genetic algorithms) dan particle swarm optimization (PSO), dan logika kabur (fuzzy logic). Penjelasan yang mendalam akan diberikan mengenai prinsip dasar, algoritma, dan kegunaan masing-masing metode dalam membangun sistem cerdas. Setelah penjelasan metode AI, pembaca akan disajikan dengan studi kasus dunia nyata yang relevan dengan metode yang dibahas. Setiap studi kasus akan memberikan gambaran tentang tantangan yang dihadapi, metode AI yang diterapkan, dan solusi yang dihasilkan. Contoh kasus tersebut akan mencakup berbagai bidang, seperti kesehatan, keuangan, transportasi, pertanian, peternakan, atau industri manufaktur.
Application of Data Augmentation on SSD Mobilenet for Detection of Kenaf Plant Disease and Pest
Authors: Agus Wahyu Widodo, Alfita Rakhmandasari, Wayan Firdaus Mahmudy, Muh Arif Rahman, Diva Kurnianingtyas
Year: 2023
Topics: Machine Learning; Computer Vision
Abstract: The Kenaf plant is a source of natural fibre and can potentially be a source of bioenergy, biopharmaceuticals, bioremediation, and others. The need for Kenaf plant fibre is increasing due to the variety of plant uses. This high demand is different from a high production level as well. One of the main production barriers is pests and diseases in plants. Rapid detection of pests and diseases is needed to carry out appropriate actions. Pest and disease detection can be assisted with deep learning methods such as Single-Shot Multibox Detection (SSD) Mobilenet. Data augmentation is carried out to improve the accuracy of the results. Although the Mobilenet SSD gets an accuracy of only 54%, using data augmentation can increase the accuracy of the Mobilenet SSD by 14.02% to 72.02%. This result is better than using Faster Region-based Convolutional Neural Network (RCNN), which is used as a comparison method.
Exploring Machine Learning Techniques for Male Infertility Prediction: A Review
Authors: Shofiyah Shofiyah, Wayan Firdaus Mahmudy
Year: 2023
Topics: Machine Learning; Healthcare
Abstract: Infertility, also known as sterility in both men and women, is a global health problem that affects the quality of life of couples who want to have children. In recent decades, technological developments in medicine and computer science have inspired the exploration of machine learning techniques to support early prediction of male infertility. In this paper, the authors present a comprehensive review of the various machine learning techniques that have been applied to male infertility prediction. The authors begin by outlining the background of male infertility and the complexity of its clinical diagnosis. We then detail the advantages of machine learning techniques in processing and analyzing complex health data, as well as their potential to provide new insights into the causative factors of male infertility. Through in-depth analysis, we identify several machine learning approaches commonly used in the literature, such …
Hybridization of Particle Swarm Optimization and Variable Neighborhood Search for Multi-Site Aggregates Production Planning in the Home Textile Industry
Authors: Agung Mustika Rizki, Gusti Eka Yuliastuti, Wayan Firdaus Mahmudy, Ishardita Pambudi Tama
Year: 2023
Topics: Optimization; Swarm Intelligence
Abstract: Aggregate production planning is an essential stage in the production process by companies because the planning stage will affect the next stage. It will be crucial if the company has several production branch locations or can be referred to as multi-site. The rules that apply and the parameter values for each production branch will undoubtedly be different from other production branches. This raises problems for the company in addition to uncertain consumer demand problems. This problem is quite complicated and very important to solve. The author resolves the problems faced by the company by applying the proposed method, namely the hybridization of Particle Swarm Optimization (PSO) and Variable Neighborhood Search (VNS). The solution generated by the proposed method proved optimum.
Optimization of information gain interval for determining artificial ripeness of banana using image data with imbalanced class
Authors: Candra Dewi, Endang Arisoesilaningsih, Wayan Firdaus Mahmudy, Solimun Solimun
Journal: Agriculture and Natural Resources
Year: 2023
Topics: Machine Learning; Image Processing
Abstract: Importance of the work: Precise determination of ripeness is crucial for post-harvest processing of fruits. Moreover, distinguishing between natural and artificial ripeness of banana fruits requires a specific feature because they have similar physical appearance. Objectives: To optimize the information gain (IG) interval to obtain the optimal features in identifying artificial banana ripeness with imbalanced class data. Materials & Methods: The test was based on six Indonesian banana cultivars using 11,593 images. In total, 78 features were extracted using morphological descriptors, a convex hull, a local binary pattern and a gray-level co-occurrence matrix. IG optimization was based on the Sturgess rule, Scott rule and K-means clustering. Oversampling based on the synthetic minority oversampling technique (SMOTE) was used to handle imbalanced data. Results: The results of the identification using extreme learning machine classification of imbalanced data showed higher accuracy based on IG optimization using the Sturgess and Scott rules rather than the use of IG. The implementation of SMOTE also substantially increased the accuracy from 20% to 40% compared to the result with imbalanced data. Most of the accuracy (80%) resulted from using selected features for four cultivars (ambon lumut, hijau, kepok and raja). The two other banana cultivars (morosebo and susu) had accuracy levels of more than 71% and 76%, respectively. Main finding: Due to the complexity of choosing the optimum number of IG bin intervals for data with very high similarity characteristics, optimization using Sturgess and Scott rules produced greater accuracy, especially …
Multi-Tier Topology Design of Wireless Sensor Networks using Multi-Objective Particle Swarm Optimization
Authors: Kasyful Amron, Wuryansari Muharini Kusumawinahyu, Syaiful Anam, Wayan Firdaus Mahmudy
Year: 2022
Topics: Wireless Networks; Optimization
Abstract: In WSN, node placement is the most fundamental and growing topic since the location and function of the nodes define network performance. Recently, the placement methods are formulated as an optimization problem and solved by multi-objective optimization (MOO) approaches. This research aims to examine the implementation of MOO for relay placement. The goals of the MOO are to develop a WSN with minimum cost and fault tolerance ability. The MOO method in this research was constructed over a swarm intelligence algorithm. The area with targets and sensors is mapped into triangular cells and relays are placed at the triangular points. This mapping allows each sensor to connect to at least two different relays and construct multi-paths to the sink with the minimum number of relays. With random sensors that cover the entire area, this method uses around 58% to 67% of all relays on average.
Validate Field Survey Data through Mobile Application (Case Study in Ministry Social Integrated Welfare Data)
Authors: Bayu Rahayudi, BH Preasetio, Eko Ganis Sukoharsono, Wayan Firdaus Mahmudy, Qomariyatus Sholihah
Journal: International Conference on Sustainability ((7th Sustainability Practitioner Conference), Bali, Indonesia
Year: 2022
Topics: Data Validation; Mobile Applications
Abstract: The objective of the study is to construct an application model of overcoming Indonesia’s Government problems in validating data resulted from field data survey in the case of social welfare data of the Ministry of Social Affairs. Since decades, there have been issues on the big gap data of invalid citizen number of eligible fund transfers from the government. The main problem is a lack of application codifying the citizen data. The research method of the study is using extreme programming software engineering methodology to build mobile application by using its features and also connected to cloud computing systems to process and calculate the data. The finding of the study construct an application of mobile model that can validate field data survey of Ministry’s Integrated Social Welfare Data continuously and timely.
Deep transformer model with pre-layer normalization for covid-19 growth prediction
Authors: Rizki Ramadhan Fitra, Novanto Yudistira, Wayan Firdaus Mahmudy
Journal: arXiv preprint arXiv:2207.06356
Year: 2022
Topics: Deep Learning; Healthcare
Abstract: Coronavirus disease or COVID-19 is an infectious disease caused by the SARS-CoV-2 virus. The first confirmed case caused by this virus was found at the end of December 2019 in Wuhan City, China. This case then spread throughout the world, including Indonesia. Therefore, the COVID-19 case was designated as a global pandemic by WHO. The growth of COVID-19 cases, especially in Indonesia, can be predicted using several approaches, such as the Deep Neural Network (DNN). One of the DNN models that can be used is Deep Transformer which can predict time series. The model is trained with several test scenarios to get the best model. The evaluation is finding the best hyperparameters. Then, further evaluation was carried out using the best hyperparameters setting of the number of prediction days, the optimizer, the number of features, and comparison with the former models of the Long Short-Term Memory (LSTM) and Recurrent Neural Network (RNN). All evaluations used metric of the Mean Absolute Percentage Error (MAPE). Based on the results of the evaluations, Deep Transformer produces the best results when using the Pre-Layer Normalization and predicting one day ahead with a MAPE value of 18.83. Furthermore, the model trained with the Adamax optimizer obtains the best performance among other tested optimizers. The performance of the Deep Transformer also exceeds other test models, which are LSTM and RNN.
Detection of Hepatoma based on Gene Expression using Unitary Matrix of Singular Vector Decomposition
Authors: Lailil Muflikhah, Nashi Widodo, Wayan Firdaus Mahmudy, Ninik Nihayatul Wahibah
Journal: International Journal of Advanced Computer Science and Applications
Year: 2021
Topics: Bioinformatics; Machine Learning
Abstract: Hepatoma is a long-term disease with a high risk of mortality. However, the disease is late detected, at the fourth level stadium due to silent symptoms. The infected hepatitis B virus gene HBx is a genome virus to trigger liver disease. This virus inserts material genetic into the host and disturbs the cell cycle. The regulation of gene expression is blocked to make work abnormal, especially for repairing and degrading. A microarray is a tool to quantify the RNA gene expression in huge volumes without any information for the related potential gene. Therefore, this study is proposed a feature extraction method using a unitary singular matrix for simplifying the classification model of hepatoma detection. Principally, the feature is decomposed using a singular vector to get the k-rank value of pattern. This matrix is applied to the representative machine learning algorithm, including KNN, Naïve Bayes, C5. 0 Decision Tree, and …
Aplikasi Jaringan Syaraf Tiruan Resilient Backpropagation pada Prediksi Magnitudo dan Lokasi Gempa Bumi
Authors: Bagus Priambodo, Wayan Firdaus Mahmudy
Year: 2020
Topics: Neural Networks; Geophysics
Abstract: Salah satu bencana yang tidak dapat dihindari adalah bencana alam. Salah satu jenis dari bencana alam, yaitu gempa bumi, merupakan bencana alam yang menyebabkan jumlah kematian rata-rata per tahun paling banyak dibandingkan bencana alam lainnya. Meskipun tak dapat dihindari, namun ada langkahlangkah antisipatif yang bisa diambil untuk meminimalisir jumlah kerugian dan korban dari gempa bumi, salah satunya adalah dengan memprediksi magnitudo gempa. Salah satu metode yang dapat digunakan untuk memprediksi magnitudo gempa di suatu tempat tersebut adalah menggunakan jaringan syaraf tiruan. Dalam penelitian ini, digunakan algoritme resilient backpropagation untuk melatih jaringan syaraf tiruan dalam memprediksi magnitudo gempa yang terjadi di Indonesia secara mingguan. Algoritme ini dipilih karena memiliki keunggulan yaitu cepat, dan tidak tergantung pada learning rate yang membatasi kemampuan belajar dari jaringan syaraf tiruan. Dengan algoritme ini, maka setiap bobot akan diperbaharui secara khusus untuk menghasilkan akurasi yang lebih maksimal. Berdasarkan hasil pengujian yang didapat dalam penelitian ini, maka jaringan syaraf tiruan yang dilatih dengan algoritme resilient backpropagation untuk memprediksi magnitudo gempa pada lokasi-lokasi di Indonesia mampu menghasilkan error terendah sebesar 0,093 dalam waktu 34,475 detik. Konfigurasi jaringan syaraf tiruan yang digunakan untuk menghasilkan hasil terbaik tersebut adalah dilatih sebanyak 100 epoch dengan menggunakan 16 neuron pada layer input dan output, serta 23 neuron pada hidden layer (hanya menggunakan …