Abstract:Abstract: Conventional semiconductor chip counting at PT Elektronik Indonesia relies on manual weighing, which is prone to human error and inefficiency. This study proposes a desktop-based counting system using a digital…
scanner and image processing. The novelty lies in integrating horizontal-vertical projection with probabilistic Hough transform to robustly detect grid lines, form square cells, and enable accurate unit estimation via average intensity analysis, eliminating the need for reference weighing. Experiments on 15 actual chip images yielded an error rate of 0.009519% and up to 73.674%time efficiency gains compared to the manual method. The system reduces operator dependency, minimizes errors, and accelerates counting, providing a practical machine vision solution for semiconductor production.
Keywords: chip counting; image processing; probabilistic hough transform; grid line detection; time effeciency.
Abstrak: Penghitungan chip semikonduktor konvensional di PT Elektronik Indonesia bergantung pada penimbangan manual, yang rentan terhadap kesalahan manusia dan kurang efisien. Penelitian ini mengusulkan sistem penghitungan berbasis desktop menggunakan scanner digital dan pengolahan citra. Kebaruan terletak pada integrasi proyeksi horizontal-vertikal dengan probabilistic Hough transform untuk mendeteksi garis grid secara kuat, membentuk sel persegi, serta memungkinkan estimasi unit akurat melalui analisis intensitas rata-rata, sehingga menghilangkan kebutuhan penimbangan referensi. Eksperimen pada 15 citra chip aktual menghasilkan tingkat kesalahan 0,009519% dan peningkatan efisiensi waktu hingga 73,674% dibandingkan metode manual. Sistem ini mengurangi ketergantungan operator, meminimalkan kesalahan, dan mempercepat penghitungan, menyediakan solusi machine vision praktis untuk produksi semikonduktor.
Kata kunci: penghitungan chip; pengolahan citra; probabilistic Hough transform; deteksi garis grid; efisiensi waktu.
Abstract:Abstract: The determination of thesis topics is a fundamental stage in academic research, yet the evaluation process remains predominantly manual and subjective. This reliance on individual lecturer perception often leads…
s to inconsistent feasibility assessments and fails to systematically measure the topic's alignment with strategic needs. This research aims to develop a Decision Support System (DSS) model based on fuzzy logic to assess the feasibility of thesis topics objectively and systematically, focusing on enhancing the relevance of student research. The research method employed the Fuzzy Inference System (FIS) with the Sugeno method. This model was designed through literature review and FGD to establish four criteria (Topic Relevance, Difficulty Level, Idea Novelty, Reference Availability) and 81 rule bases. The model validation results against expert judgment using 15 test data showed a high accuracy rate of 91.31%, with a Mean Absolute Percentage Error (MAPE) value of 8.69%. In conclusion, this DSS model is proven to be valid and consistent, and it can be relied upon as an objective tool to improve the quality and relevance of thesis topics.
Keywords: academic evaluation; decision support system; fuzzy logic; fuzzy sugeno; thesis feasibility
Abstrak: Penentuan topik skripsi merupakan tahapan fundamental dalam penelitian akademik, namun proses evaluasinya hingga kini masih cenderung manual dan subjektif. Ketergantungan pada persepsi dosen secara individu sering kali menyebabkan penilaian kelayakan yang tidak konsisten serta kegagalan dalam mengukur keselarasan topik dengan kebutuhan strategis secara sistematis. Penelitian ini bertujuan mengembangkan model Sistem Pendukung Keputusan (SPK) berbasis logika fuzzy untuk menilai kelayakan topik skripsi secara objektif dan sistematis, dengan fokus pada peningkatan relevansi penelitian mahasiswa. Metode penelitian yang digunakan adalah Fuzzy Inference System (FIS) dengan metode Sugeno. Model ini dirancang melalui tinjauan pustaka dan Focus Group Discussion (FGD) untuk menetapkan empat kriteria (Relevansi Topik, Tingkat Kesulitan, Kebaruan Ide, Ketersediaan Referensi) serta 81 basis aturan. Hasil validasi model terhadap penilaian pakar menggunakan 15 data uji menunjukkan tingkat akurasi yang tinggi yaitu 91,31%, dengan nilai Mean Absolute Percentage Error (MAPE) sebesar 8,69%. Kesimpulannya, model SPK ini terbukti valid dan konsisten, serta dapat diandalkan sebagai alat objektif untuk meningkatkan kualitas dan relevansi topik skripsi.
Kata kunci: evaluasi akademik; sistem pendukung keputusan; logika fuzzy; fuzzy sugeno; kelayakan skripsi
Abstract:Abstract: The rapid expansion of digital learning environments has increased students’ reliance on Learning Management Systems (LMS), including SPOT UPI. However, limited studies have examined the platform’s overall user…
user experience across all User Experience Questionnaire (UEQ) dimensions. This study aims to evaluate the user experience (UX) of SPOT UPI, identify its strengths and weaknesses, and provide recommendations for system improvement. A quantitative-dominant mixed-method design was applied, involving 81 student respondents for the UEQ survey and two participants for follow-up semi-structured interviews selected through purposive sampling. The UEQ data were analyzed to generate mean scores for six UX dimensions, while interview data were thematically analyzed to support the interpretation of quantitative findings. The results indicate that Perspicuity (1.05) and Efficiency (0.78) achieved the highest scores, reflecting adequate clarity and functionality. In Contrast, Stimulation (0.50) and Novelty (-0.15) were the lowest, indicating limited engagement and innovation. Overall, pragmatic quality (0.84) outperformed hedonic quality (0.17), suggesting that users value functionality more than enjoyment. In conclusion, SPOT UPI is generally usable but lacks aesthetic appeal, emotional engagement, and innovative features, highlighting the need for interface redesign and performance optimization to enhance the overall learning experience.
Keywords: learning management system; user experience; user experience questionnaire
Abstrak: Perkembangan pembelajaran digital membuat mahasiswa semakin bergantung pada Learning Management System (LMS), termasuk SPOT UPI. Meski digunakan secara luas, evaluasi pengalaman pengguna secara komprehensif berdasarkan seluruh dimensi User Experience Questionnaire (UEQ) masih belum banyak dilakukan. Penelitian ini bertujuan untuk mengevaluasi user experience (UX) pada SPOT UPI, mengidentifikasi keunggulan dan kelemahannya, serta memberikan rekomendasi perbaikan sistem. Penelitian menggunakan desain penelitian mixed-method dominan kuantitatif, melibatkan 81 responden pada survei UEQ dan dua partisipan pada wawancara semi-terstruktur yang dipilih melalui purposive sampling. Data UEQ dianalisis untuk memperoleh nilai rata-rata pada enam dimensi UX, sedangkan data wawancara dianalisis secara tematik untuk memperkaya interpretasi temuan kuantitatif. Hasil menunjukkan bahwa Perspicuity (1,05) dan Efficiency (0,78) menjadi dimensi dengan skor tertinggi, mencerminkan bahwa SPOT UPI mudah dipahami dan cukup membantu dalam menyelesaikan tugas. Sebaliknya, Stimulation (0,50) dan Novelty (-0,15) memperoleh skor terendah, menandakan rendahnya tingkat keterlibatan dan inovasi yang dirasakan pengguna. Secara keseluruhan, pragmatic quality (0,84) lebih tinggi dibandingkan hedonic quality (0,17), menunjukkan bahwa pengguna lebih mengutamakan aspek fungsional daripada kenyamanan emosional. Temuan tersebut mengindikasikan bahwa SPOT UPI sudah layak digunakan secara fungsional, tetapi masih memerlukan peningkatan pada interface, pengalaman visual, dan fitur inovatif agar dapat memberikan pengalaman belajar digital yang lebih menarik dan optimal.
Kata kunci: learning management system; pengalaman pengguna; user experience questionnaire
Abstract:Abstract: Soil moisture is an important factor in determining the watering needs of plants for optimal growth. Therefore, accurate monitoring of soil moisture is necessary. This research aims to design and build a soil moisture…
oisture detection tool based on the Decision Tree algorithm with the support of the YL-69 sensor for humidity measurement and the DHT11 sensor for temperature measurement to increase data accuracy. This system uses NodeMCU ESP8266 as a microcontroller and is integrated with an Android application as a user interface. Sensor interpretation data is analyzed using the Decision Tree algorithm to determine soil conditions (dry, damp or wet). The test results show an accuracy level of 95% from 300 data samples. Thus, this system is able to detect soil moisture effectively and can help increase the efficiency of crop management on a household and commercial agricultural scale.
Keywords: agriculture, android, decision tree algorithm, sensors, soil moisture detection
Abstract:Abstract: The increasing use of digital banking applications has led to the need for a deeper understanding of user perceptions, especially through aspect-based sentiment analysis. This study aims to classify the sentiment…
nt of SeaBank app users by focusing on four main aspects: learnability, efficiency, technical issues or errors, and satisfaction. Review data totaling 1,971 comments were collected from the Google Play Store and labeled with sentiments based on the scores (ratings) given by users. The CRISP-DM approach serves as the methodological framework for this study, which includes five classification algorithms: Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, and Random Forest. The evaluation results show that the SVM algorithm provides the best performance with the highest average value of the four aspects achieving accuracy of 93.91%, Precision of 91.16%, recall of 97.96% and F1-Measure of 94.33%. According to the research findings, the Support Vector Machine (SVM) algorithm provides the best performance when performing aspect-based sentiment analysis on text data from digital banking application reviews. The findings are expected to serve as a reference for the development of automated evaluation systems that rely on user opinions as the basis for decision making.
Keywords: aspects; CRISP-DM; digital Banking; seabank; sentiment analysis
Abstrak: Peningkatan pemakaian aplikasi perbankan digital mendorong perlunya pemahaman yang lebih dalam mengenai persepsi pengguna, terutama melalui analisis sentimen berbasis aspek. Penelitian ini bertujuan untuk mengklasifikasikan sentimen pengguna aplikasi SeaBank dengan berfokus pada empat aspek utama: kemudahan dipelajari (learnability), efisiensi penggunaan (efficiency), kendala atau kesalahan teknis (error), serta tingkat kepuasan (satisfaction). Data ulasan berjumlah 1.971 komentar dikumpulkan dari Google Play Store dan diberi label sentimen berdasarkan skor (rating) yang diberikan oleh pengguna. Pendekatan CRISP-DM berfungsi sebagai kerangka metodologis untuk penelitian ini, yang mencakup lima algoritma klasifikasi: Naïve Bayes, Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Decision Tree, dan Random Forest. Hasil evaluasi menunjukkan bahwa algoritma SVM memberikan performa terbaik dengan nilai rata-rata dari ke empat aspek tertinggi yang mencapai accuracy sebesar 93.91%, Precision sebesar 91.16%, recall sebesar 97.96% dan F1-Measure sebesar 94.33%. Menurut temuan penelitian, algoritma Support Vector Machine (SVM) memberikan kinerja terbaik saat melakukan analisis sentimen berbasis aspek pada data teks dari ulasan aplikasi Seabank. Temuan ini diharapkan dapat menjadi referensi bagi pengembangan sistem evaluasi otomatis yang mengandalkan opini pengguna sebagai dasar pengambilan keputusan.
Kata kunci: Analisis Sentimen, Aspek, Bank Digital, SeaBank, CRISP-DM
Abstract:Abstract: The rapid growth of the cosmetics industry on e-commerce platforms has intensified competition, creating a critical need for effective, data-driven marketing strategies. This study aims to conduct a comparative…
analysis of machine learning algorithms to predict the sales categories (High, Medium, Low) of cosmetic products on the Tokopedia marketplace. Four classification models; Random Forest, XGBoost, Logistic Regression, and Naive Bayes were trained and evaluated on data collected via web scraping. The methodology incorporates the Synthetic Minority Over-sampling Technique (SMOTE) to address significant class imbalance and GridSearchCV for hyperparameter optimization to ensure a fair and robust comparison. The experimental results conclusively show that the Random Forest model achieved the best performance, yielding the highest F1-Score Macro Average of 0.75 and an accuracy of 85.3%. The superior model was subsequently implemented in a simple recommendation system to simulate optimal discount strategies, demonstrating its practical utility in providing actionable insights for business decisions.
Keywords: classification; comparative analysis; machine learning; sales prediction; SMOTE
Abstrak: Pertumbuhan pesat industri kosmetik pada platform e-commerce telah membuat persaingan ketat, sehingga menciptakan kebutuhan krusial akan strategi pemasaran yang efektif dan berbasis data. Penelitian ini bertujuan untuk melakukan analisis komparatif terhadap algoritma machine learning untuk memprediksi kategori penjualan (Tinggi, Sedang, Rendah) produk kosmetik di marketplace Tokopedia. Empat model klasifikasi, yaitu Random Forest, XGBoost, Regresi Logistik, dan Naive Bayes, dilatih dan dievaluasi menggunakan data yang dikumpulkan melalui web scraping. Metodologi penelitian ini menerapkan Synthetic Minority Over-sampling Technique (SMOTE) untuk mengatasi ketidakseimbangan kelas yang signifikan dan GridSearchCV untuk optimisasi hyperparameter guna memastikan perbandingan yang adil. Hasil eksperimen menunjukkan bahwa model Random Forest mencapai performa terbaik, dengan menghasilkan F1-Score Macro Average tertinggi sebesar 0,75 dan akurasi 85,3%. Model unggul ini kemudian diimplementasikan dalam sebuah sistem rekomendasi sederhana untuk menyimulasikan strategi diskon yang optimal, yang menunjukkan kegunaan praktisnya dalam memberikan wawasan yang dapat ditindaklanjuti untuk pengambilan keputusan bisnis.
Kata kunci: analisis komparatif; klasifikasi; machine learning; prediksi penjualan; SMOTE
Abstract:Abstract: The rapid development of information and communication technology has prompted the government to provide digital-based services, including in the management of demographic data. This study aims to apply the Task-Centered…
k-Centered System Design (TCSD) method in designing the Muara Enim Regency Demographic Data Information System. The TCSD method was chosen to ensure that the prototype design process was systematic and focused on user needs and tasks. The research stages included identification, user-centered needs analysis, scenario-based design, and walkthrough evaluation. The designed prototype supports several main tasks, including viewing demographic statistics, searching for specific data, submitting data download requests, and contacting the admin. The evaluation was conducted through online usability testing using the Maze platform with the System Usability Scale (SUS) instrument involving 13 respondents. The evaluation results showed an average SUS score of 78.5, which falls into the “good” category. This confirms that the interface design has met usability standards, is user-friendly, and is capable of supporting user needs in accessing and managing demographic data. Thus, the application of the TCSD method has proven to be effective in producing an interface design that is focused on user tasks and can be the basis for further system development.
Keywords: system usability scale; task centered system design; user interface
Abstract:Abstract: YouTube is one of the most popular video streaming platforms, but it has constraints that can cause problems when clients have difficulty finding content according to their wishes. The main objective of this study…
udy is to increase user capacity in viewing content specifically in the field of women's empowerment. By using content-based filtering techniques, the system will analyze user preferences and interests through recommendations for women's empowerment content. The data source is via the YouTube API and is analyzed using PHP programming content-based filtering techniques. The system's recommendations provide a list of women's empowerment content with a user request display. The results of the research evaluation obtained a precision value of 62%, meaning that the recommendations match the topic being searched for, namely women's empowerment. The recall value of 84% indicates that the system has succeeded in finding relations from the database. The f1-score value of 72% indicates that there is a balance between precision and recall, meaning that a system is needed that is not only accurate but also complete. While the cosine value shows a score of 0.7071 approaching the maximum value (1.0). The recommendation of the content-based filtering method produces quite effective women's empowerment content.
Keywords: content-based filtering, recommendations, women Empowerment, youtube
Abstract:Abstract: Posyandu cadres play an important role in supporting community health services at the village and sub-district levels. However, the selection process for the best cadres is often carried out subjectively without…
t clear and standardized criteria. This condition can lead to a decline in service quality and reduced cadre motivation. Therefore, a decision support system is needed to provide assessments that are objective, measurable, and accountable. This study aims to optimize the Posyandu cadre selection process in Lubuk Kilangan District through the development of a decision support system based on a hybrid method: Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The AHP method is applied to determine the weight of each selection criterion based on its level of importance through pairwise comparisons. Subsequently, TOPSIS is used to rank candidates according to their proximity to the ideal solution. The methodology includes a literature review, primary data collection through interviews and questionnaires with stakeholders (community health centers, cadres, and village officials), as well as the implementation and testing of the AHP–TOPSIS–based system.
Keywords: Posyandu, Cadre, AHP, TOPSIS, Decision Support System
Abstrak: Kader Posyandu memiliki peran penting dalam mendukung layanan kesehatan masyarakat di tingkat desa dan kelurahan. Namun, proses pemilihan kader terbaik masih sering dilakukan secara subjektif tanpa acuan kriteria yang jelas dan terstandarisasi. Kondisi ini dapat mengakibatkan penurunan kualitas pelayanan serta rendahnya motivasi kader. Oleh karena itu, dibutuhkan suatu sistem penunjang keputusan yang mampu memberikan hasil penilaian yang objektif, terukur, dan dapat dipertanggungjawabkan. Penelitian ini bertujuan untuk mengoptimalkan proses pemilihan kader Posyandu di Kecamatan Lubuk Kilangan melalui pengembangan sistem penunjang keputusan berbasis metode hybrid Analytical Hierarchy Process (AHP) dan Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Metode AHP digunakan untuk menetapkan bobot masing-masing kriteria pemilihan kader berdasarkan tingkat kepentingan melalui perbandingan berpasangan. Selanjutnya, metode TOPSIS digunakan untuk melakukan pemeringkatan calon kader berdasarkan kedekatannya terhadap solusi ideal. Metode yang digunakan dalam penelitian ini mencakup studi literatur, pengumpulan data primer melalui wawancara dan kuesioner kepada stakeholder terkait (puskesmas, kader, dan perangkat desa), serta implementasi dan pengujian sistem berbasis AHP-TOPSIS.
Kata kunci: Posyandu, Kader, AHP, TOPSIS, Sistem Pendukung Keputusan
Abstract:Abstract: Ineffective drug demand management can lead to problems such as imbalanced drug distribution, excess stock, or shortages in community health centers. To address this, data mining can be utilized to support the…
planning and control process of drug inventory. Clustering techniques were chosen because they are able to group drug data based on certain characteristics, thus identifying stable and unstable drug supply patterns. This study aims to group drug data at Simpang Kawat Community Health Center in Jambi City, which can be used as a reference in planning drug needs in the next period. Data grouping is divided into three categories: slow-moving, medium-moving, and fast-moving. The research data includes attributes of drug name, initial stock, receipt, inventory, usage, and final stock, with a total of 1758 data sets, which were processed using the CRISP-DM framework through the RapidMiner application. Cluster quality evaluation was carried out using the Davies-Bouldin Index (DBI). The results showed that the K-Means algorithm obtained a DBI value of 0.175, smaller than K-Medoids which obtained a value of 0.354. Because a smaller DBI value indicates better cluster quality, K-Means provides more optimal clustering results than K-Medoids. Through these clustering results, community health centers can utilize drug cluster information to support more efficient drug procurement planning, as well as reduce the risk of excess or shortage of stock.
Keywords: data mining; clustering; k-means; k-medoids; davies-bouldin index