Rancang bangun mesin pengupas tempurung kelapa

The first work that must be done on coconuts after the coir stripping process is to strip the coconut shells. This is because the part of the coconut that is processed further is part of the coconut meat itself. Coconut shell is the hardest part of the coconut fruit. On this coconut shell, coconut meat is attached. Manually removing coconut meat will take a relatively long time, around 3-5 minutes for 1 coconut. Of course, with this time the capacity and production efficiency cannot be maximized. This research was conducted with the aim of designing and testing the capabilities of coconut shell peeling machines. This machine is operated by using 2 blades, namely a flat peeler blade as a barrier to the surface of the coconut shell and a toothed peeler to press the surface of the shell. The results of the functional testing of this machine obtained the best value at the rotational speed of the peeler teeth of 14 rpm with an average shelling time of 20.96 seconds per piece.Keywords: coconut, machine, coconut shells, shelling time

FINE-GRAINED SENTIMENT ANALYSIS IN SOCIAL MEDIA USING GATED RECURRENT UNIT WITH SUPPORT VECTOR MACHINE

Social media platforms are widely used to share opinions, leading to a large growth of text data on the internet. This data can be a key source of up-to-date and inclusive information by conducting sentiment analysis. Typically, sentiment analysis research classifies binary based on the polar values generated. However, this has its limitations, such as classifying sentences containing positive and negative expressions, leading to incorrect predictions. Fine-grained sentiment analysis provides more precise results by associating values with more than two classification targets. The objective of this study is to carry out sentiment analysis at a fine-grained level related to public policy in Indonesia using the GRU-SVM model with feature extraction and expansion techniques. However, sentiment analysis research still faces challenges in NLP. Deep learning have successfully overcome the challenges of traditional machine learning models in terms of efficiency and performance. This study proposes GRU-SVM model. GRU is used because it can adaptively control dependencies, making it more efficient in memory usage, while SVM is used as it is state-of-the-art in sentiment analysis. Result of the study show that the selection of word representation techniques, the addition of feature extraction techniques, datasets, data ratios, and feature expansion are crucial in the model testing process. The GRU-SVM model achieved the best performance with an accuracy of 96.02%. Overall, the results of this study demonstrate that the GRU-SVM method is effective in analyzing sentiments in Indonesian tweets

Estimating Forest Carbon Stocks Using CNN and Vegetation Texture Features Extracted from UAV and Satellite Data in Telkom University

Forests play a crucial role in mitigating climate change by acting as carbon sinks, yet traditional methods of carbon stock estimation, reliant on manual tree measurements, are costly, time-consuming, and geographically limited. Recent advancements in remote sensing technologies, such as the combination of Unmanned Aerial Vehicles (UAVs) and Google Earth Engine (GEE), offer a promising alternative by integrating high-resolution local observations with global-scale data. Using the power of Convolutional Neural Networks (CNNs), this study suggests an integrated method for classifying carbon stocks by fusing textural parameters like homogeneity and entropy with spectral indices like Green Chromatic Coordinates (GCC) and Excess Green Index (ExG). CNNs are used to capture the spectral richness and structural complexity of vegetation because of their propensity to extract hierarchical spatial characteristics. The research compares the performance of various feature combinations—color-based, texture-based, and mixed features—using a hybrid framework of UAV and GEE data. It is anticipated that the results will demonstrate how spectral and textural features work together to increase classification accuracy. In addition to tackling major issues in carbon stock estimation, this scalable and integrated framework is made to adapt to a variety of forest ecosystems and aid in the creation of conservation policies and the mitigation of climate change

Film Recommendation System Using Content-Based Filtering and the Convolutional Neural Network (CNN) Classification Methods

Managing large amounts of data is a challenge faced by users, so a recommendation system is needed as an information filter to provide relevant item suggestions. Twitter is often used to find information about movie reviews that can be used a basis for developing recommendation systems. This research contributes to applying content-based filtering in the context of Convolutional Neural Network (CNN). To the best of the researcher's knowledge, there has been no research addressing this combination of method and classification. The main focus is to evaluate the development of a recommendation system by integrating and comparing similarity identification methods using the RoBERTa and TF-IDF approaches. In this research, Roberta and TF-IDF as vectorizer and classification methods are applied to form a model that can recognize patterns in data and produce accurate predictions based on its features. The total data used is 854 movies and 34086 film reviews from 44 Twitter accounts. The SMOTE method was applied as a technique to overcome data imbalance. The research was conducted three times with increasing accuracy results. The first experiment TF-IDF as baseline, SMOTE on CNN classification. The second experiment, applying baseline, SMOTE, embedding on CNN classification. The third experiment applied baseline, SMOTE, embedding, and optimizer to CNN classification. The experimental results show that TF-IDF as baseline, SMOTE, embedding and SGD optimizer with the best learning rate on CNN classification can provide optimal results with an accuracy rate of 86.41%. Thus, the system can provide relevant movie recommendations with good prediction accuracy and performance

Movie Recommender System on Twitter Using Weighted Hybrid Filtering and GRU

The development of the industry in the film sector has experienced rapid growth, marked by the emergence of film streaming platforms such as Netflix and Disney+. With the abundance of available films, users face difficulty in choosing films that suit their preferences. Recommender systems can be a solution to this problem for users. Recommender systems rely on user reviews, making Twitter a platform that can be used to collect user reviews of a film. This study will develop a recommender system that has the potential to provide item recommendations to users using the weighted hybrid filtering and GRU methods. The weighted hybrid filtering used is a combination of collaborative filtering and content-based filtering methods. The dataset used in this study was obtained by crawling tweets relevant to the feedback of specific accounts regarding a film. The dataset resulting from the data crawling consists of a total of 854 films, 45 users and 34,086 tweets consisting of film reviews from Twitter users. The GRU model classification is performed on the results of weighted hybrid filtering with model optimization involving testing various test size scenarios and optimizer methods. The test sizes used are 40%, 30%, and 20%. The optimizer methods used include Adam, Nadam, Adamax, Adadelta, Adagrad, and SGD. The research results show that the optimal outcome is obtained using the Nadam optimization method. The performance evaluation yielded 85.74% precision, 88.63% recall, 88.63% accuracy, and 86.30% F1-score

PANDUAN KURIKULUM BERBASIS OBE/KKNI/SKKNI APTIKOM PROGRAM STUDI SARJANA INFORMATIKA/ILMU KOMPUTER

Permendikbud Nomor 3 tahun 2020 tentang Standar Nasional Pendidikan Tinggi (SNDikti) menyatakan bahwa kurikulum adalah seperangkat rencana dan pengatura mengenai tujuan, isi, dan bahan pelajaran serta cara yang digunakan sebagai pedoman penyelenggaraan kegiatan pembelajaran untuk mencapai tujuan Pendidikan Tinggi. Pentingnya kurikulum dalam mencapai lulusan yang berkualitas menjadi dasar bagi APTIKOM untuk melakukan pemutakhiran Buku Kurikulum APTIKOM 2019 agar selaras dengan perkembangan zaman, tuntutan global untuk mulai menerapkan kurikulum berbasis Outcome Based Education (OBE), tuntutan ACM/IEEE 2020, dan jenjang kualifikasi KKNI/SKKNI. Hasil dari kerja tim Forum Prodi APTIKOM adalah Buku Kurikulum Bidang INFOKOM Berbasis OBE/KKNI/SKKNI. Buku ini akan terus disempurnakan, seiring dengan perjalanan waktu dan kebutuhan penyempurnaan dan pemutakhiran. Untuk saat ini, Buku Kurikulum Bidang INFOKOM Berbasis OBE/KKNI/SKKNI adalah buku versi 1.0

CONTENT-BASED FILTERING CULINARY RECOMMENDATION SYSTEM USING DEEP CONVOLUTIONAL NEURAL NETWORK ON TWITTER (X)

Along with the development of technology, social media has become integral to everyday life, especially for sharing content like culinary reviews. Social media platform X (formerly Twitter) is often used for sharing culinary recommendations, but the abundance of information makes it difficult for users to find relevant suggestions. In order to improve rating prediction performance, this study suggests a recommendation system model that is more thoroughly created utilizing Content-Based Filtering (CBF) combined with Deep Convolutional Neural Network (CNN) and optimised with Particle Swarm Optimization (PSO). Data was collected from PergiKuliner and Twitter, totaling 2644 reviews and 200 cuisines. The preprocessing involved text processing, translation, and polarity assessment. Post-labeling, 7438 data were labeled with 0 and 1562 with 1. Label 0 means not recommended while label 1 means recommended. The imbalance is handled by applying the SMOTE method after observing that the fraction of data labeled 0 and 1 is 65.2%. CBF employed TF-IDF feature extraction and FastText word embedding, while Deep CNN handled classification. PSO optimisation was applied to enhance the accuracy of culinary rating predictions. The results showed an initial accuracy of 76.32% with the baseline Deep CNN model, which increased to 86.06% after Nadam optimisation with the best learning rate, and further reached 86.18% after PSO optimisation on dense units. The 9.86% accuracy improvement from the baseline model demonstrates the effectiveness of the combined methods

SENTIMENT ANALYSIS USING CONVOLUTIONAL NEURAL NETWORK (CNN) AND PARTICLE SWARM OPTIMIZATION ON TWITTER

Over time, social media has always changed quickly. People can voice their ideas on various topics and communicate with each other through social media. One social media platform that allows users to express their ideas through tweets is Twitter. Sentiment is the route via which each person can express their ideas on a variety of subjects. The sentiment can be positive or negative. Sentiment analysis can be used to determine how Twitter users feel about particular subjects. Sentiment analysis on popular subjects in 2023, specifically the 2024 presidential contenders, will be done in this research. The dataset used in this research consists of 37,391 entries with 5 keywords. The research aims to understand how Twitter users respond and express their sentiments towards the presidential candidate through the use of deep learning classification techniques with Convolutional Neural Network (CNN), feature extraction using Term Frequency-Inverse Document Frequency (TF-IDF) method, and feature expansion with Word2Vec. Furthermore, this study employs Particle Swarm Optimization as an optimization technique to enhance the sentiment analysis model's performance. The test's results demonstrate a high degree of accuracy, offering a comprehensive picture of Twitter users' sentiments and perspectives toward the 2024 presidential contenders. This research helps to understand the dynamics of public opinion in the political context. Based on the evaluation results of the research, it yielded an accuracy of 78.2%, showcasing an improvement of 10.07% compared to the baseline