Lab-IS/filkom-research-papers huggingface dataset:
Efficient Scheduling of Plantation Company Workers using Genetic Algorithm.
Authors: Wayan Firdaus Mahmudy, Andreas Pardede, Agus Wahyu Widodo, Muh. Arif Rahman
Journal: Knowl. Eng. Data Sci.
Year: 2020
Topics: Optimization; Genetic Algorithms
Abstract: Scheduling is an activity carried out by allocating certain resources intended to perform a job or task related to time. Scheduling is a part of the industry's decision-making process to allocate existing data or resources to be utilized more optimally [1]. Good scheduling is required by a company that works in the field of plantation and garden management. The company has a number of tasks such as planning all the care of plants and green areas, providing all fertilizer and plant material needs to ensure all plants are growing well and productive, harvesting crops, and maintaining the stock of fertilizer. Plantation activities are carried out by plantation workers whose schedules are determined by the company. The density of worker activities must be balanced with efficient and fair work scheduling. A good schedule will minimize worker dissatisfaction and work stress while maintaining physical health [2][3]. The schedule should consider a fair allocation of working time in holidays, ensuring all tasks are assigned to a worker, and even the number of working days of each worker every month. Scheduling that involves complex constraints is a challenging task. Several methods have been proposed in the literature. Meta-heuristic algorithms are often applied as is have the capability to deal with complex constraints. Examples of the algorithms that have been applied for the complex scheduling are ant colony optimization algorithm [4][5], simulated annealing [6][7], tabu search [8], particle swarm optimization [9][10], variable neighborhood search [11][12][13], and genetic algorithm [14][15][16].
Weight Optimization of The Neural Fuzzy System (NFS) Using Genetic Algorithm for Forecasting
Authors: Nadia Roosmalita Sari, Wayan Firdaus Mahmudy, Aji P Wibawa, Gayatri Dwi Santika
Year: 2019
Topics: Neural Networks; Optimization
Abstract: Inflation is a phenomenon of increasing prices on a continuous basis which results in the increase of other goods. This study proposes the Neural Fuzzy System (NFS) as a method to predict the rate of inflation in Indonesia. To improve the accuracy, the weight at this stage of Neural Network to be determined correctly. So, this research using Genetic Algorithms to determine the best weights in the training process. This weight can be used to obtained output thorough testing process. Then, it can be processed again in the next step using FIS Sugeno until obtained the end forecasting result. To increase more accurate forecasting results, the establishment of fuzzy rules must be specified correctly. It takes a novelty that minimizes the number of fuzzy rules by dividing the initial parameter into the two (positive and negative) on stage Neural Network. So, the fuzzy rules generated less. To measure the accuracy of the …
Milkfish Feed Optimization Using Adaptive Particle Swarm Optimization (PSO) Algorithm
Authors: Beryl Labique Ahmadie, Wanda Athira Luqyana, Wayan Firdaus Mahmudy, Rio Arifando
Year: 2019
Topics: Optimization; Swarm Intelligence
Abstract: Milkfish is a cultivation commodity that contributes quite significantly on a national scale. But in practice, milkfish cultivation requires a large number of production costs, especially in the provision of fish feed. This research will discuss how to optimize the composition of milkfish feed, minimize costs without lowering production yields, and still pay attention to the nutritional needs of milkfish cultivation. The optimization method, Particle Swarm Optimization (PSO) is applied to the formulation process and the composition of milkfish feed to meet the nutritional needs of fish with minimal cost. Improvements to the PSO resulted in Adaptive Particle Swarm Optimization (APSO). This improvement conducted to strengthen PSO's ability to explore broad areas of search for solutions efficiently. The results of computational analysis prove that APSO produces a better solution than PSO.
Penerapan Extreme Learning Machine (ELM) untuk Peramalan Laju Inflasi di Indonesia
Authors: FI INDONESIA
Journal: Jurnal Teknologi Informasi dan Ilmu Komputer (JTIIK)
Year: 2019
Topics: Machine Learning; Economics
Abstract: Inflasi merupakan salah satu indikator untuk mengukur perkembangan suatu bangsa. Apabila inflasi tidak terkontrol akan memberikan banyak dampak negative terhadap masyarakat disuatu negara. Ada banyak cara untuk mengendalikan inflasi, salah satunya dengan peramalan. Peramalan adalah suatu aktivitas untuk mengetahui kejadian di masa mendatang berdasarkan data masa lalu. Pada penelitian ini menggunakan metode kecerdasan buatan yakni extreme learning machine (ELM). Kelebihan ELM yaitu cepat dalam proses pembelajaran. Berdasarkan penggujian yang dilakukan metode ELM mendapatkan nilai kesalahan sebesar 0.0202008, lebih kecil dibandingkan dengan metode backpropagation sebesar 1.16035821. Hal tersebut membuktikan bahwa metode ELM sangat cocok digunakan untuk peramalan.
Opportunities of Artificial Intelligence for Authentication and Assurance of Halal Products
Authors: Wayan Mahmudy, Diva Kurnianingtyas
Journal: Journal of Information Technology and Computer Science
Year: 2025
Topics: Artificial Intelligence; Food Technology
Abstract: The need for Muslims to obtain halal food encourages the development of systems that can assist in the authentication and assurance of halal products. Various Artificial Intelligence (AI) based systems have been developed to meet these needs. AI is a technology that enables computers to understand, learn, and perform tasks that would typically require human intelligence and reasoning to act and make decisions. With the help of high-speed computers, AI algorithms learn patterns in available data and perform assigned tasks with high accuracy and efficiency. This study focusses on the application of AI for the identification, monitoring, verification and validation of halal products. Also, this paper limits the discussion to food products with the topics of (1) Inspection of raw materials and detection of contamination to ensure the halalness of raw materials (2) Market monitoring and recommendation of halal restaurants to help Muslim consumers. The consumers were looking for a restaurant that suits their needs and preferences (3) Verification of halal certification to ensure that the certification given to the product comes from a competent and trustworthy authority.
Redefining Competitiveness: The Role of Lean Production and Eco-Design in the Indonesian FMCG Industry
Authors: Christina Rachmawati, Wayan Firdaus Mahmudy, Moh Khusaini, Andi Kurniawan
Journal: Jurnal Pembangunan dan Alam Lestari
Year: 2025
Topics: Production Management; Sustainability
Abstract: The Indonesian FMCG market is booming due to increased consumer spending power. However, this expansion brings challenges, including fierce competition and the potential for escalating production costs. FMCG companies must prioritize innovation to improve operational efficiency and environmental sustainability to stay competitive and profitable. This research explores the strategies employed by a leading Indonesian FMCG company to attain a competitive edge in sustainability by integrating eco-design and lean production principles. The concept of Eco-Design, which emphasizes minimizing environmental impact over the product's lifecycle, is integrated with Lean Production's emphasis on waste reduction and process efficiency. This company is redefining competitiveness by merging these two strategies. It's not just about economic success; it's about achieving that success in a way that prioritizes environmental stewardship and minimizes ecological impact. This holistic strategy seeks to harmonize financial and environmental objectives, creating a mutually reinforcing cycle that fosters a more sustainable and responsible approach to competitive advantage. This study used statistical analysis to confirm that Lean Production and Eco-Design strongly contribute to sustainable manufacturing in the FMCG sector. The findings highlight how combining these two approaches can boost efficiency and sustainability, offering a powerful roadmap for companies in Indonesia and beyond to achieve lasting success in a rapidly changing market.
Optimizer Performance Test on CNN Long Short-Term Memory Network for Car Sales Forecasting
Authors: Ninis Agung Wahyu Prayoga, Wayan Firdaus Mahmudy
Journal: Journal of Applied Statistics and Data Science
Year: 2025
Topics: Deep Learning; Optimization
Abstract: In the automotive industry, forecasting future demand is particularly crucial due to the complexity of production processes and supply chains. This article examines the comparative performance of a hybrid CNN-LSTM model for car sales forecasting, utilizing seven optimization algorithms: Adam, RMSprop, SGD, Adagrad, Adadelta, Adamax, and Nadam. Each optimization method has its own advantages. For instance, Adam offers fast convergence, while RMSprop is more effective in handling large gradient fluctuations. Adagrad is well-suited for managing gradient magnitude variations, whereas Adadelta addresses Adagrad’s limitations. Adamax is ideal for models with a broader parameter space, and Nadam combines Nesterov Accelerated Gradient and Adam, making it suitable for tasks requiring both momentum and adaptive learning. This study demonstrates that the CNN-LSTM model optimized with Nadam delivers the best performance, achieving a Mean Squared Error (MSE) of 35,383.14 and a Root Mean Squared Error (RMSE) of 188.10. In comparison, traditional methods such as ARIMA yield an MSE of 59,105.94 and an RMSE of 243.11. These findings indicate that the CNN-LSTM model optimized with Nadam outperforms conventional time series forecasting methods in predictive accuracy.
EfficientNetV2-S Enhancement to Classify Acute Lymphoblastic Leukemia: Integrating Pre-Trained Models and Grid Search for Optimal Performance.
Authors: Arif Muntasa, Rima Tri Wahyuningrum, Alisa Sugiarti, Muhammad Yusuf, Abdelwahed Motwakel, Deshinta Arrova Dewi, Yuli Panca Asmara, Zabrina Tuzzahra, Wayan Firdaus Mahmudi
Journal: International Journal of Intelligent Engineering & Systems
Year: 2025
Topics: Deep Learning; Medical Imaging
Abstract: Leukemia is one of the deadliest types of cancer. The hospitals have been working on several occasions to conduct early screenings of the preventable death. Unfortunately, leukemia detection is very expensive. This study tries to classify Leukemia images to find the best value of the hyperparameter using the Grid Search method processed using Pre-trained EfficientNetV2-S. We have modified the activation function ReLu6 (Variant of the Rectified Linear Unit has value less than 6) to SeLu6 (Variant of the Scaled Exponential Linear Unit has value less than 6) to help the network maintain stable statistical properties during training. Our proposed model has five major stages: Input layer, Stem Layer, Mobile Inverted Bottleneck Convolution (MBConv), Fused Mobile Inverted Bottleneck Convolution (Fused-MBConv) and Head layer. We use a total of five different Fused-MBConv layers. The depth-wise convolutional …
Improved Fuzzy Possibilistic C-Means using Artificial Bee Colony for Clustering New Student’s Financial Capability to Determine Tuition Level
Authors: Edi Satriyanto, Ni Wayan Surya Wardhani, Syaiful Anam, Wayan Firdaus Mahmudy
Journal: JOIV: International Journal on Informatics Visualization
Year: 2066
Topics: Machine Learning; Optimization
Abstract: Outliers in the dataset will affect the quality of the cluster, so a good clustering method is needed. Based on the Mahalanobis distance method, it is known that the research dataset has outliers. Clustering methods that are often used for this type of data are Fuzzy C-means (FCM), Possibilistic C-means (PCM), and Fuzzy Possibilistic C-means (FPCM). This study aims to develop a clustering method that is more robust to outliers by using the Artificial Bee Colony (ABC) algorithm to minimize the objective function of FPCM. This study produces a new algorithm called Artificial Bee Colony Fuzzy Possibilistic C-Means (ABCFPCM) so that the resulting clusters are not easily trapped in the local optimum. This study also provides cluster centroid initialization using K-Means++ to improve cluster quality. ABCFPCM performs best because it significantly increases the Silhouette value and the Between Sum Squares (BSS) and Total Sum Squares (TSS) ratio. ABCFPCM performance provides the best cluster quality of 72.16% based on the BSS/TSS ratio, FPCM of 70.71%, and FCM K-Means++ of 68.14%. K-Means++ in the cluster method does not affect cluster performance except for FCM, where cluster quality is slightly increased. The centroid results of 8 clusters as the best performance of ABCFPCM are used to determine the tuition rate level. The impact of this study is to improve the quality of FPCM performance because it is no longer trapped in a local optimum at the cluster centroid.
Dimensional reduction using principal component analysis by alternating least squares on fuzzy possibilistic C-means for new student economic data
Authors: Edi Satriyanto, Ni Wayan Surya Wardani, Syaiful Anam, Wayan Firdaus Mahmudy
Journal: AIP Conference Proceedings
Year: 2024
Topics: Machine Learning; Dimensionality Reduction
Abstract: Cluster analysis on datasets that have outlier data is very influential on the quality of clustering.. Clustering methods for dealing with outlier data are Fuzzy C-Means (FCM), Possibilistic C Means (PCM) and Fuzzy Possibilistic C-Means (FPCM). Weakness in FCM that it still sensitive to outlier. PCM has the ability to cluster on outlier data but has the potential to produce identical clusters if the distance between cluster centers is close to others. In this study, a mixed scale dataset was used, namely ratio, nominal and ordinal. Outlier detection was carried out on the dataset using Mahalanobis Distance, it was found that 6.5% of the dataset had outliers. Principal Component Analysis was by Alternating Least Squares (PRINCALS) method to reduce the dimensions of the mixed scale. Based on the ratio Between Sum of Squares (BSS) and Total Sum of Squares (TSS), the performance results of FPCM with PRINCALS were …
Fault-tolerant and minimum communication cost wireless sensor networks using modified genetic algorithm
Authors: K Amron, WM Kusumawinahyu, S Anam, WF Mahmudy
Journal: AIP Conference Proceedings
Year: 2024
Topics: Wireless Sensor Networks; Optimization
Abstract: The performance of a network, including wireless sensor networks, is determined by various aspects, including the position and function of its nodes. The node placement methods, especially relay, are an interesting topic as they were the key to enhancing WSN communication ability. Relay placement is usually associated with energy efficiency, coverage area, connectivity, and communication costs. Dozens of recent surveys have even explored other aspects, including reliability, scalability, and security. As the optimization aspects become more complex, researchers consider that the multiobjective optimization (MOO) approach is the most promising solution. This research aims to implement a Multi-Objective Optimization (MOO) method of relay placement on a large scale to construct a WSN with minimum cost and fault tolerance ability. From the point of view of WSN, there are at least two novelties: the area …
Classification of Ornament or Pepatran in Balinese Traditional Architecture Pattern Using Learning Vector Quantization Algorithm
Authors: I Gusti Agung Gede Arya Kadyanan, Nyoman Gunantara, Ida Bagus Gede Manuaba, Komang Oka Saputra, Wayan Firdaus Mahmudy
Year: 2024
Topics: Machine Learning; Pattern Recognition
Abstract: Bali is very famous for traditional architectur. Not everyone is able to identify the various Balinese ornament patterns or Balinese pepatran just based on their visual attributes. This is because the patterns have high similarities even though they have different classes. To solve this problem, a machine learning model was developed using the LVQ method. In this model, preprocessing is carried out involving scaling, grayscale conversion and sobel edge detection to obtain strong features and enable the model to obtain high accuracy. A total of 210 pepatran images, which 168 pepatran images or 80% of the images used as training data and 42 or 20% of the pepatran images used as testing data. Twelve classes representing each used pepatran comprise the data in this research. The model has been shown to be effective in identifying Balinese pepatran patterns. Performance testing and Accuracy Testing were two …
Integration of Frequency Control and Water Quality Monitoring System Into Fish Caller Device Based on IoT.
Authors: Gatot Ciptadi, Wayan Firdaus Mahmudy
Journal: International Journal on Engineering Applications
Year: 2024
Topics: IoT; Water Quality Monitoring
Abstract: Seawater pollution is highly detrimental to human life and marine biota. Quality of seawater in a particular location also affects fish caught by fishermen. This is the reason why early detection of water conditions is crucial for the preservation of the marine ecosystem with a focus on the measurements of temperature, pH, and salinity. Separate researches have been conducted on the application of sensors and IoT to measure seawater quality as well as fish caller device to increase the possibility of catching fish. Therefore, this research has been used to integrate both devices in order to ensure fishermen contribute to the provision of information on seawater quality while catching fish by using fish caller device. This has been achieved through the implementation of NodeMCU ESP8266 to control frequency of fish caller device as well as to assess the parameters of seawater quality. Moreover, data obtained from the …
Identification of arabica coffee post-harvest processing using a convolutional neural network
Authors: Mas’ud Effendi, Maulana Muhamad Faqy, Imam Santoso, Retno Astuti, Wayan Firdaus Mahmudy
Journal: BIO Web of Conferences
Year: 2024
Topics: Deep Learning; Agriculture
Abstract: Indonesia’s economy is greatly boosted by coffee, one of its flagship commodities. The post-harvest processing of coffee involves various processes, and the different methods have a crucial connection to the subsequent stages. Digital image analysis using Convolutional Neural Network (CNN) methods can be utilized to improve the identification of coffee beans. This study uses CNN with the ResNet-18 and MobileNetV2 architectures for image analysis. The research results show that the MobileNetV2 architecture produces the best accuracy of 98.89% at a data proportion of 70:20:10, and the ResNet-18 architecture produces the best accuracy of 99.56% at a data proportion of 50:25:25. This shows that both of them can handle differences in data proportions well in identifying the post-harvest process of Arabica coffee. The choice between the two can be considered based on available computational resources …
An Enhanced Backpropagation Neural Network for Fire Alarm Detection
Authors: M Fadli Ridhani, Wayan Firdaus Mahmudy
Year: 2023
Topics: Neural Networks; Safety Systems
Abstract: Fires are frequent emergencies, threatening human safety and causing considerable losses. Fire alarm detection systems are essential in preventing fires and minimizing more severe losses. However, current fire alarm detection systems suffer from one main problem: a high false alarm rate, which often results from overfitting of models. This occurs due to imbalanced or noisy data. To address these issues, an Enhanced Backpropagation Neural Network (BPNN) approach is proposed and evaluated in this study. The Enhanced BPNN utilizes various parameter sets to optimize its performance. Artificial Neural Networks (ANNs) with Backpropagation (BP) are known for their self-learning ability, adaptiveness, and relatively fast processing. In this study, the BPNN model is extensively experimented with by varying the number of layers, neurons, activation functions, and learning rates. To assess the reliability of the …
Riset Optimasi Industri Manufaktur Menggunakan Metode Meta-Heuristic
Authors: Wayan Firdaus Mahmudy
Journal: Prosiding Seminar Nasional Teknik Elektro, Sistem Informasi, dan Teknik Informatika (SNESTIK)
Year: 2023
Topics: Optimization; Meta-Heuristics
Abstract: • Metode Heuristik dan Meta-Heuristik• Genetic Algorithm (GA)-1960• Ant Colony Optimization (ACO)-1992• Particle Swarm Optimization (PSO)-1995• Variable Neighborhood Search (VNS)-1997• Harmony Search Algorithm (HS)–2001• Artificial Bee Colony Algorithm (ABC)-2005• Firefly Algorithm (FA)-2008
Comparison of Multi-layer Perceptron and Support Vector Machine Methods on Rainfall Data with Optimal Parameter Tuning
Authors: Agus Widodo, Wayan Firdaus Mahmudy, Maulana Muhamad Arifin
Journal: International Journal of Advanced Computer Science and Applications
Year: 2023
Topics: Machine Learning; Parameter Tuning
Abstract: This study describes the search for optimal hyperparameter values in rainfall data in 49 cities in Australia, consisting of 145,460 records with 22 features. The process eliminates missed values and selects 16 numeric type features as input features and one feature (Rain Tomorrow) as output feature. It is processed using the Multi-Layer Perceptron (MLP) and Support Vector Machine (SVM) methods based on Three Best Accuration (3BestAcc) and Best Three Nearest Neighbors (3BestNN). The results showed that the SVM kernel linear method gave an average accuracy value of 0.85586 and was better than the MLP method with an accuracy of 0.854.
Penentuan Kesesuaian Lahan Budidaya Buah Apel Di Kota Batu menggunakan Fuzzy Inference System Tsukamoto
Authors: Farhanna Mar'i, Irvi Oktanisa, Ullump Pratiwi, Wayan Firdaus Mahmudy
Journal: Indexia: Informatics and Computational Intelligent Journal
Year: 2022
Topics: Fuzzy Systems; Agriculture
Abstract: Penurunan produksi tanaman buah apel disebabkan oleh semakin berkurangnya lahan untuk melakukan budidaya buah apel di kota Batu. Untuk memaksimalkan produksi, maka petani perlu memilih lahan yang tepat. Untuk memilih lahan yang tepat bukanlah hal yang mudah sehingga penggunaan Fuzzy Inference System (FIS) menggunakan metode Tsukamoto dapat mempermudah petani menentukan lahan yang layak untuk membudidayakan buah apel. Pada penelitian ini digunakan empat kriteria utama yang dibutuhkan untuk menentukan kesesuaian lahan yaitu curah hujan, kedalaman efektif perakaran, kelerengan, dan erosi. Output yang dihasilkan pada penelitian ini merupakan hasil penentuan lahan tanam dengan kategori sangat sesuai, sesuai marginal, cukup sesuai, dan tidak sesuai dengan kondisi masukan. Berdasarkan perhitungan akurasi sistem yang diukur menggunakan aturan yang didapatkan dari literatur pada penelitian ini menghasilkan tingkat akurasi sebesar 100%.
Optimization of agricultural product storage using real-coded genetic algorithm based on sub-population determination
Authors: Wayan Firdaus Mahmudy, Nindynar Rikatsih, Syafrial Syafrial
Journal: IAES International Journal of Artificial Intelligence
Year: 2022
Topics: Optimization; Genetic Algorithms
Abstract: The storage of fresh agricultural products is a combinatorial problem that should be solved to to maximize number of items in the storage and also maximize the total profit without exceed the capacity of storage. The problem can be addressed as a knapsack problem that can be classified as NP-hard problem. We propose a genetic algorithm (GA) based on subpopulation determination to address the problem. Sub-population GA can naturally divide the population into a set of sub-population with certain mechanism in order to obtain a better result. GA based on sub-population is applied by generating a set of sub-population which is happened in the process of initializing population. A special migration mechanism is developed to maintain population diversity. The experiment shows GA based on sub-population determination provide better results comparable to those achieved by classical GA.
The Role of SME’s Focused Tech Startup on Indonesian SME’s Adoption of Industry 4.0
Authors: IP Tama, WS Nugroho, WF Mahmudy
Year: 2022
Topics: Industry 4.0; Tech Startups
Abstract: ASEAN SME has a role as the regional socioeconomic stabilizer. This particular role is inseparable from endogenous multi-sector collaboration. Although, Indonesian SMEs were struggled in adopting Industry 4.0 correspond to digital infrastructure and digital literacy problems. This study evaluates Indonesian SME collaboration dynamics with government and technology startup (TS). The integration of agent-based model and causal loop simulation were employed to assess the TS collaboration impact on SME Industry 4.0 adoption and SME competition with larger competitors. The simulation results imply the SME collaboration with TS can lead to early adoption of Industry 4.0 which balances the business competition environment. The model also shows rising the government aid exponentially can help the SME to late adoption of Industry 4.0 which unable to sustain the SME in business competition. Thus, the developed integrative simulation model is a state-action planning model with each state result bounded to the previous state result that determined by initial input parameters. Conclusively, the model can be used as a resiliency planner for SME Industry 4.0 adoption.
The Evaluation of Technology Startup Role on Indonesian SMEs Industry 4.0 Adoption Using CLD-ABM Integrated Model. Sustainability 2022, 14, 8462
Authors: IP Tama, WS Nugroho, WF Mahmudy, P Purnami
Year: 2022
Topics: Industry 4.0; Computational Modeling
Abstract: The role of the Association of Southeast Asia Nations (ASEAN) small and medium enterprises (SMEs) as the regional socioeconomic stabilizer is inseparable from endogenous multisector collaboration. Indonesian SMEs struggled with Industry 4.0 adoption due to the lower digital infrastructure (DI) and digital literacy (DL) index. This study aims to develop a provisional model to explore the role of technology startups (TS) in covering the DI and DL of SMEs. The evaluation was through a simulation model of Indonesian SMEs’ collaboration with the government and TS. The developed model is a concurrent real-time integration of an agent-based model (ABM) with a causal loop diagram (CLD). The simulation results imply that SMEs’ collaboration with TS leads to the early adoption of Industry 4.0 that balances the business competition environment. The model also shows that exponentially rising government aid can help the SMEs into late adoption of Industry 4.0. However, SMEs are still unable to sustain the business competition. Thus, the integrative simulation model is a state-action planning model with each state result bounded to the previous state result. The initial input parameters determine the model behavior. Thus, the model is a good resiliency planner for SMEs’ Industry 4.0 adoption.
DETEKSI HAMA DAN PENYAKIT PADA TANAMAN KENAF BERDASARKAN DAUN MENGGUNAKAN METODE FASTER REGIONAL BASED CONVOLUTIONAL NEURAL NETWORK
Authors: Alfita Rakhmandasari
Year: 2021
Topics: Deep Learning; Computer Vision
Abstract: Tanaman kenaf merupakan tanaman serat diambil kulit batangnya dan digunakan sebagai bahan baku pembuatan geo-tekstil, pulp, fiber drain, papan partikel, papan serat, dan kertas kualitas tinggi. Hal yang mempengaruhi menurunnya perkembangan adalah produksinya yang rendah dikarenakan lahan yang tepat untuk perkembangan terbatas, munculnya bahan plastik, harga sarana untuk produksi tinggi, harga serat relatif rendah, serta gangguan dari hama dan penyakit. Di Indonesia, hama dan penyakit yang saat ini banyak ditemukan adalah hawar daun, sundapteryx, dan tungau. Oleh karena itu perlu dilakukan pendeteksian dini untuk serangan hama dan penyakit agar bisa dikendalikan dengan baik. Sistem deteksi dini bisa dilakukan dengan menggunakan metode object detection. Pada penelitian ini, Faster Regional Based Convolutional Neural Network (Faster RCNN) digunakan untuk membantu pendeteksian hama dan penyakit pada tanaman kenaf. Model pengujian terbaik mendapat akurasi deteksi sebesar 77.5% dan akurasi bounding box sebesar 96.74%.
Klasifikasi Citra Ikan Menggunakan Adaptif Learning Rate Pada Metode Transfer Learning
Authors: Rizka Suhana, M Wayan Firdaus, B Agung Setia
Year: 2021
Topics: Machine Learning; Transfer Learning
Abstract: Karagaman jenis ikan pada ekosistem terumbu karang menjadi salah satu indikasi dalam penentuan kesehatan di ekosistem terumbu karang. Satu usaha yang terbaik untuk melestarikan ekosistem terumbu karang yaitu konservasi perairan. Banyak para pakar di Balai Badan Penelitian dan Pengembangan Perikanan dan Kelautan sering melakukan klasifikasi citra ikan dengan cara manual. Pada pakar melakukan proses klasifikasi tidak efektif karena terlalu membutuhkan waktu lama dan terdapat kesalahan dalam memprediksi jenis ikan dengan perkembangan zaman sekarang bisa menggunakan teknologi yang mutakhir. Teknik handal dalam melakukan klasifikasi citra yaitu Convolutional Neural Network (CNN). Seiring waktu berjalan tentu banyak orang menginginkan pembelajaran yang cepat dan pemecahan masalah baru dengan lebih cepat dan lebih baik, sehinga munculah transfer learning, yang mengadopsi bagian dari CNN yaitu convolution layer yang telah dimodifikasi. Mengamati kebutuhan para pakar dibidang konservasi kelautan, sehingga peneliti memutuskan untuk menyelesaikan permasalahan ini dengan menggunakan arsitektur dari CNN yaitu pretrained model dari Mobilenet V2. Pemilihan arsitektur pretrained model dari Mobilenet V2 ini handal dan mampu menyelesaikan permasalahan pada klasifikasi citra ikan karena selebelumnya arsitektur dari Mobilenet V2 telah dilatih dengan 1000 jenis gambar dari ImageNet. Selain itu dari penelitian yang dilakukan oleh peneliti arsitektur ini ringan karena proses komputasi tidak terlalu banyak menggunakan resource memory pada saat melatih data citra, sehingga …
PENGEMBANGAN MEDIA PEMBELAJARAN PENGENALAN BANGUN DATAR UNTUK ANAK TUNAGRAHITA BERBASIS AUGMENTED REALITY DENGAN PENDEKATAN THINK-PAIR-SHARE
Authors: M Munandar, Eng Herman Tolle, Wayan Firdaus Mahmudy
Year: 2021
Topics: Augmented Reality; Educational Technology
Abstract: Tunagrahita merupakan anak berkebutuhan khusus yang memiliki kekurangan dalam kecerdasan intelektual yang dibawah rata-rata. Pendidikan anak tunagrahita berbeda dengan anak normal karena harus menggunakan guru yang khusus yang dapat menerjemahkan sebuah perintah ke anak tunagrahita. Pembelajarannya harus bersifat interaktif karena anak tunagrahita daya tangkap yang lemah. Tujuan dari penelitian ini adalah mampumenghasilkan aplikasi yang dapat membantu dalam pengenalanbangun datar kepada anak tunagrahita yang dapat diterapkan di kehidupan sehari-hari. Uji coba dalam penelitian menggunakan mirip One group Pretest-posttes design (Sugiono). Dalam one group pretest-posttest design mengunakan pengujian pretest dilanjutkan treatment dilanjutkan posttest, sedangkan pada penelitian menggunakan treatment dengan metode pembelajaran konvensional, dilanjutkan dengan pretes, dilanjutkan dengan treatment dengan metode yang diusulkan yaitu Think-share-pair. Pemilihan metode uji ini bertujuan untuk membandingkan hasil dari treament pembelajaran dengan metode konvensional dengan treament pembelajaran dengan metode Think-share-Pair dengan bantuan aplikasi. Kesimpulan yang dapat ditarik dari hasil penelitian yang telah dilakukan yaitu dari hasil analisis uji beda (uji-t) dalam media pembelajaran berbasis Augmented reality dengan pendekatan think-pair-share pengenalan bangun datar mengunakan metode think-pair-share untuk meningkatkan daya tangkap dan cognitive anak tunagrahita adalah signifikan. Nilai pretest rata-rata sebesar 78,778 dan nilai posttest 80,444 yang …
Evolution strategies based coefficient of TSK fuzzy forecasting engine
Authors: Nadia Roosmalita Sari, Wayan Firdaus Mahmud, Aji Prasetya Wibawa
Journal: International Journal of Advances in Intelligent Informatics
Year: 2021
Topics: Fuzzy Systems; Evolutionary Algorithms
Abstract: Evolution Strategies (ES) is a specialization of the evolutionary algorithm. ES works very well with optimization problems [1]. in various areas from 2000 to the present [2]. ES is a population-based algorithm suitable for complex mathematical model-based issues [3]. This method is similar to GA, has a high success rate in resolving the optimization problems [4]. ES performs well in generating the ruleset of Intrusion Detection Systems (IDS)[5] and effective for large-scale vehicle routing problems with time windows [6]. ES algorithm is explicitly designed for high-dimensional searching space [7]. The result showed that ES could provide the best solution to 86% of all test instances within reasonable computing times. Evolution Strategies is suitable for nonlinear problems, such as forecasting. Forecasting estimates future occasions or values based on past and current facts [8]. It uses an irregular and inconsistent pattern of historical data as a base of time-series analysis [9]. The techniques are various, including Fuzzy Inference System (FIS) Takagi Sugeno Kang (TSK) method with time-series analysis [10], which is effective for time-series such as inflation rate.
Identification of Potential Gene Expression in Hepatitis B Virus-induced Liver Cancer Using the Simulated Annealing Algorithm
Authors: Lailil Muflikhah, Wayan Firdaus Mahmudy
Year: 2021
Topics: Bioinformatics; Optimization
Abstract: Results This study aimed to identify the potential genes that could be used as predictors to detect liver cancer using a heuristic algorithm, simulated annealing optimization. The basic idea of this algorithm was to overcome the combinatorial problem using probability values to select the significant features as a predictor for the classifier model in the representative machine learning algorithms, such as SVM, KNN, Naïve Bayes. C5. 0 Decision Tree, and Random Forest. The experimental results showed quite high performance, more than 90% on average. However, using the simulated annealing algorithm requires substantial computation time to identify genes that could be used for detecting liver cancer.
Optimasi Penjadwalan Pekerja Kebun Menggunakan Algoritme Genetika
Authors: Andreas Patuan G Pardede, Wayan Firdaus Mahmudy
Journal: Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer
Year: 2020
Topics: Genetic Algorithms; Operations Research
Abstract: Optimasi Penjadwalan Pekerja Kebun Menggunakan Algoritme Genetika | Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer j-ptiik logo Beranda Terkini Arsip Tentang Kami Tentang Jurnal Ini Penyerahan Naskah Dewan Editor Kontak Bahasa Indonesia Register Login Optimasi Penjadwalan Pekerja Kebun Menggunakan Algoritme Genetika 1.Beranda / 2.Arsip / 3.Vol 4 No 13 (2020): Publikasi Khusus Tahun 2020 / 4.Artikel Optimasi Penjadwalan Pekerja Kebun Menggunakan Algoritme Genetika Penulis Andreas Patuan G. Pardede Fakultas Ilmu Komputer, Universitas Brawijaya Wayan Firdaus Mahmudy Fakultas Ilmu Komputer, Universitas Brawijaya Kata Kunci: Penjadwalan Karyawan, Optimasi, Algoritma Genetika Abstrak Artikel dipublikasikan di Jurnal Nasional Terakreditasi, Knowledge Engineering and Data Science Diterbitkan 21 Dec 2020 Cara Mengutip Format Sitasi Lainnya Terbitan Vol 4 …
Asymmetric City Tour Optimization in Kediri Using Ant Colony System
Authors: Abidatul Izzah, Benni A Nugroho, Wayan F Mahmudy, Fitra A Bachtiar
Journal: Jurnal Nasional Teknik Elektro dan Teknologi Informasi
Year: 2020
Topics: Optimization; Ant Colony Optimization
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.
Improving Performance for Prediction of Hepatocellular Carcinoma Using Stacking Method of Support Vector Machine.
Authors: Lailil Muflikhah, Agus Wahyu Widodo, Wayan Firdaus Mahmudy, Solimun
Year: 2020
Topics: Machine Learning; Support Vector Machines
An Effective Chromosome Representation on Proportional Tuition Fees Assessment Using NSGA-II
Authors: Farid Jauhari, Wayan Firdaus Mahmudy, Achmad Basuki
Journal: Journal Homepage: www. jitecs. ub. ac. id
Year: 2019
Topics: Genetic Algorithms; Multi-Objective Optimization
Abstract: Proportional tuition fees assessment is an optimization process to find a compromise point between student willingness to pay and institution income. Using a genetic algorithm to find optimal solutions requires effective chromosome representations, parameters, and operator genetic to obtain efficient search. This paper proposes a new chromosome representation and also finding efficient genetic parameters to solve the proportional tuition fees assessment problem. The results of applying the new chromosome representation are compared with another chromosome representation in the previous study. The evaluations show that the proposed chromosome representation obtains better results than the other in both execution time required and the quality of the solutions.
Good Parameters for PSO in Optimizing Laying Hen Diet.
Authors: Gusti Ahmad Fanshuri Alfarisy, Wayan Firdaus Mahmudy, Muhammad Halim Natsir
Journal: International Journal of Electrical & Computer Engineering (2088-8708)
Year: 2088
Topics: Particle Swarm Optimization; Agricultural Technology
Abstract: Manual formulation of poultry diet by taking into account the fulfillment of all nutrients requirement with least cost is a difficult task. Particle Swarm Optimization (PSO) shows promising technique to solve this problem. However, there is a lack of studying a good parameter for PSO to solve feed formulation problem since PSO is sensitive to control parameter which depends on the problem. Therefore, this study investigates good swarm size, total iterations, acceleration coefficients, and inertia weight to produce a better formula. PSO with proposed good parameters is compared with other parameters. The obtained result shows that PSO with good parameters choice produces the highest fitness. Furthermore, good parameters of PSO can be used as a reference for a software developer and for further research to optimize poultry diet using PSO.