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Perbandingan Tiga Skema Kombinasi Hyper Parameter Convolutional Neural Networks dalam Klasifikasi Biji Kopi Hasil Roasting Luther Alexander Latumakulita
Jurnal Nasional Komputasi dan Teknologi Informasi (JNKTI) Vol 7, No 3 (2024): Juni 2024
Publisher : Program Studi Teknik Komputer, Fakultas Teknik. Universitas Serambi Mekkah

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32672/jnkti.v7i3.7580

Abstract

Abstrak - Indonesia merupakan negara kelima dengan konsumen kopi tertinggi di dunia. Selain enak, kopi juga bermanfaat untuk meningkatkan metabolisme tubuh. Nilai konsumen kopi yang bertambah harus dibarengi dengan peningkatan mutu hasil roasting biji kopi. Tujuan dari penelitian ini yaitu untuk membangun sistem kecerdasan buatan yang dapat melakukan klasifikasi biji kopi robusta setelah proses roasting menggunakkan algoritma Convolutional Neural Network (CNN). Sebanyak 450 data biji kopi dari 3 class klasifikasi dilatih menggunakan 3 skema dengan kombinasi nilai epoch, batch size, dan learning rate yang berbeda. Hasil ekseprimen menunjukan bahwa skema dengan kombinasi hyper parameter dengan nilai epoch sebesar 200, batch size sebesar 16, dan learning rate sebesar 0,0001 menghasilkan akurasi testing tertinggi 96% dibandikan dengan kedua skema lainnya yang menghasilkan akurasi testing berturut-turut sebesar 95% dan 93%. Model klasifikasi yang dihasilkan menunjukan performansi system yang sangat baik menandakan model yang ditemukam dalam research ini dapat dipakai untuk mensortir kualitas biji kopi hasil roasting sehingga dapat berdampak positip dalam memajukan industry rosting kopi Indonesia..Kata kunci: CNN, Deep Learning, Klasifikasi, Biji Kopi Robusta Abstract - Indonesia is the fifth country with the highest coffee consumers in the world. Apart from being delicious, coffee is also useful for increasing the body's metabolism. The increasing consumer value of coffee must be accompanied by an increase in the quality of roasted coffee beans. The aim of this research is to build an artificial intelligence system that can classify robusta coffee beans after the roasting process using the Convolutional Neural Network (CNN) algorithm. A total of 450 coffee bean data from 3 classification classes were trained using 3 schemes with different combinations of epoch, batch size and learning rate values. The experimental results show that the scheme with a combination of hyper parameters with an epoch value of 200, a batch size of 16, and a learning rate of 0.0001 produces the highest testing accuracy of 96% compared to the other two schemes which produce testing accuracy of 95% and 93% respectively.  The resulting classification model shows very good system performance, indicating that the model found in this research can be used to sort the quality of roasted coffee beans so that it can have a positive impact in advancing the Indonesian coffee rosting industry.Keywords : CNN, Deep Learning, Classification, Robusta Coffee Beans
Sistem Pakar Diagnosa Penyakit Lambung Menggunakan Metode Forward Chaining Dan Certainty Factor Scheryl Pongantung; Marline Sofiana Paendong; Luther Alexander Latumakulita
Indonesian Journal of Intelligence Data Science Vol 3 No 2 (2024): Volume 3 No 2 2024
Publisher : Faculty of Mathematics and Natural Sciences Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/ijids.v3i2.50076

Abstract

Limited knowledge about the early symptoms of stomach diseases has motivated the author to develop a system that helps the community obtain information. This system aims to provide assistance to the public in obtaining information, consultation, and early treatment for stomach diseases without having to have direct meetings with experts. The expertise of a medical professional in diagnosing stomach diseases can be implemented into an application. In this Expert System, Forward Chaining method is used for reasoning and the Certainty Factor method is used to calculate confidence levels. Based on data processing from one of the users, the research results show that GERD is the most likely diagnosis, with a Certainty Factor value of 96.5%.
Pemilihan Ukuran Kail Optimal Berbasis Karakteristik Ikan Laut Menggunakan Metode AHP-SAW: Studi Kasus di Perairan Sekitar Kota Manado Sanriomi Sintaro; Frangky Jessy Paat; Luther Alexander Latumakulita
Jurnal Komputasi Vol. 13 No. 1 (2025)
Publisher : Jurusan Ilmu Komputer Fakultas MIPA Universitas Lampung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.23960/komputasi.v13i1.289

Abstract

Keberhasilan kegiatan penangkapan ikan dengan metode pancing sangat dipengaruhi oleh pemilihan ukuran dan jenis kail yang sesuai. Setiap spesies ikan memiliki karakteristik biologis yang berbeda, seperti berat tubuh dan ukuran mulut, yang harus dipertimbangkan dalam pemilihan kail agar aktivitas penangkapan menjadi lebih efisien dan berkelanjutan. Penelitian ini bertujuan untuk mengembangkan model pengambilan keputusan yang sistematis dalam pemilihan ukuran kail optimal untuk berbagai spesies ikan. Metode yang digunakan adalah kombinasi Analytic Hierarchy Process (AHP) untuk menentukan bobot kriteria dan Simple Additive Weighting (SAW) untuk merangking alternatif ukuran kail berdasarkan kriteria tersebut. Empat kriteria utama yang dipertimbangkan meliputi kesesuaian ukuran mulut ikan, kapasitas berat maksimum kail, kekuatan bahan kail, dan ketersediaan kail di pasaran. Proses SAW dilakukan secara spesifik untuk setiap spesies ikan, dengan mempertimbangkan karakteristik biologis masing-masing ikan sebagai tahap penyaringan awal. Hasil penelitian menunjukkan bahwa ukuran kail optimal sangat bervariasi tergantung pada spesies ikan target. Ikan berukuran besar seperti Tuna dan Marlin direkomendasikan menggunakan ukuran kail besar (9/0, 8/0), sementara ikan kecil seperti Roa dan Baronang lebih sesuai dengan ukuran kail kecil. Model AHP-SAW yang dibangun terbukti efektif dalam memberikan rekomendasi ukuran kail yang lebih objektif, sistematis, dan aplikatif. Temuan ini diharapkan dapat membantu meningkatkan efisiensi dan keberlanjutan praktik penangkapan ikan di lapangan. Ke depan, validasi lapangan bersama komunitas nelayan direncanakan untuk menguji efektivitas model ini di praktik penangkapan nyata. Selain itu, pengembangan sistem rekomendasi otomatis berbasis aplikasi diharapkan dapat meningkatkan penerapan model ini secara praktis di kalangan pelaku perikanan.
Aspect-Based Sentiment Analysis of Public Opinion on the Free Nutritious Meal Program using BERTopic on X Carmen Emanuela Dwiva Lisapaly; Luther Alexander Latumakulita; Rillya Arundaa
Journal of Artificial Intelligence and Technology Information (JAITI) Vol. 4 No. 2 (2026): Volume 4 Number 2 June 2026
Publisher : PT. Tech Cart Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.58602/jaiti.v4i2.245

Abstract

This study aims to analyze public opinion on the Free Nutritious Meal (MBG) Program on the X platform using an Aspect-Based Sentiment Analysis (ABSA) approach with BERTopic-based aspect extraction. Unlike previous studies that primarily perform sentiment classification at the overall text level, this study identifies specific aspects within public discussions to provide more fine-grained insights. Twitter data were collected and preprocessed, followed by topic modeling using BERTopic to extract topics that were subsequently defined as aspects. Topic quality was evaluated using topic coherence (c_v) and topic diversity metrics. The modeling process initially produced 36 topics with a coherence score of 0.4446 and a diversity score of 0.8541. After relevance-based selection, 18 topics were retained as aspects, with the coherence score increasing from 0.4446 to 0.5370 and the diversity score increasing from 0.8541 to 0.8611. Sentiment labeling was then performed using the Twitter-XLM-RoBERTa model to determine the distribution of positive, negative, and neutral sentiments across each aspect. The results demonstrate that the proposed ABSA approach with BERTopic-based aspect extraction provides a more structured and insightful mapping of public opinion, enabling the identification of aspects with the highest levels of support and indications of opposition toward the MBG Program. These findings are expected to serve as a basis for consideration in data-driven policy evaluation and support more informed decision-making.
PENERAPAN RULE-BASED EXPERT SYSTEM UNTUK REKOMENDASI UMPAN BERDASARKAN SPESIES IKAN DAN KONDISI PERAIRAN DI LAUT MANADO Sanriomi Sintaro; Luther Alexander Latumakulita; Vederico Pitsalitz Sabandar
JRIS : Jurnal Rekayasa Informasi Swadharma Vol 5, No 2 (2025): JURNAL JRIS EDISI JULI 2025
Publisher : Institut Teknologi dan Bisnis (ITB) Swadharma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.56486/jris.vol5no2.824

Abstract

Capture fisheries are an important economic sector for coastal communities in the Manado region. One of the key factors influencing fishing success is the selection of appropriate bait. Choosing the correct bait can significantly improve catch efficiency, while incorrect bait selection may reduce fishing effectiveness. In practice, bait selection among local fishermen still relies heavily on traditional knowledge passed down orally or gained through personal experience, which is not always easily accessible to novice fishermen. This study aims to develop a Rule-Based Expert System that provides bait selection recommendations to support fishing activities around Manado. The system was designed by incorporating practical knowledge from experienced fishermen obtained through semi-structured interviews and field observations. The parameters used in the system include target fish species, water depth, sea current strength, and season. The Rule base was constructed based on various combinations of these parameters and implemented as a web-based application using PHP programming language. Testing results show that the system achieves a 90% accuracy rate in providing recommendations based on validation conducted with local fishermen. Further evaluation indicates that the system is considered easy to use and beneficial as a decision-support tool for bait selection, particularly for novice fishermen. Additionally, the fishermen provided positive feedback for future system enhancements, suggesting the inclusion of additional contextual factors such as weather conditions and lunar phases. In conclusion, the developed Rule-Based Expert System has significant potential to support more efficient and sustainable fishing practices in the waters around Manado and facilitate knowledge transfer to the next generation of fishermen.Perikanan tangkap merupakan sektor ekonomi yang sangat penting bagi masyarakat pesisir di wilayah Manado. Salah satu faktor kunci yang mempengaruhi hasil tangkapan ikan adalah pemilihan umpan yang tepat. Umpan yang sesuai dapat meningkatkan peluang keberhasilan penangkapan, sementara pemilihan umpan yang kurang sesuai dapat menyebabkan penurunan efisiensi usaha penangkapan. Dalam praktiknya, pemilihan umpan oleh nelayan di wilayah ini masih sangat bergantung pada pengetahuan tradisional yang diperoleh melalui pengalaman pribadi atau diwariskan secara lisan, sehingga tidak selalu mudah diakses oleh nelayan pemula. Penelitian ini bertujuan untuk mengembangkan sebuah Rule-Based Expert System yang dapat memberikan rekomendasi pemilihan umpan untuk mendukung kegiatan perikanan tangkap di perairan sekitar Manado. Sistem dirancang dengan mengadopsi pengetahuan praktis dari nelayan berpengalaman, yang diperoleh melalui proses wawancara semi-terstruktur dan observasi lapangan. Parameter yang digunakan dalam sistem meliputi spesies ikan target, kedalaman perairan, arus laut, dan musim. Rule base disusun berdasarkan kombinasi keempat parameter tersebut, kemudian diimplementasikan dalam sebuah aplikasi berbasis web menggunakan bahasa pemrograman PHP. Hasil pengujian menunjukkan bahwa sistem mampu memberikan tingkat kesesuaian rekomendasi sebesar 90%, berdasarkan validasi yang dilakukan bersama nelayan di wilayah Manado. Evaluasi lebih lanjut menunjukkan bahwa sistem ini dinilai mudah digunakan, serta bermanfaat sebagai alat bantu dalam proses pengambilan keputusan terkait pemilihan umpan, terutama bagi nelayan pemula. Selain itu, nelayan memberikan masukan positif terkait pengembangan sistem ke depan, termasuk penambahan faktor-faktor lain seperti kondisi cuaca dan fase bulan. Dengan demikian, Rule-Based Expert System yang dikembangkan dalam penelitian ini memiliki potensi besar untuk mendukung perikanan tangkap yang lebih efisien dan berkelanjutan di perairan sekitar Manado, serta membantu proses transfer pengetahuan kepada generasi nelayan baru
Design and Evaluation of a Decision Support System for Classifying Tourism Site Crowding and Recommending Governance Responses in Bunaken National Park Aditya Kalua; Mochamad Agung Wibowo; Luther Alexander Latumakulita
Jurnal Testing dan Implementasi Sistem Informasi Vol. 4 No. 1 (2026): Jurnal Testing dan Implementasi Sistem Informasi
Publisher : Lembaga Riset dan Inovasi Almatani

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.55583/jtisi.v4i1.2232

Abstract

Effective governance of marine protected areas (MPAs) requires reliable mechanisms to translate multidimensional ecological and social data into coordinated institutional action. Despite widespread adoption of carrying capacity frameworks, a significant "implementation gap" persists between theoretical conservation thresholds and operational decision-making at the site level. This study addresses that gap by designing, implementing, and evaluating a Decision Support System (DSS) artifact tailored for Bunaken National Park (BNP), Indonesia. Grounded in Design Science Research (DSR) principles, the artifact employs a deterministic, rule-based classification engine that processes four normalized input dimensions visitor density, social carrying capacity, infrastructure load, and governance readiness to compute a Composite Crowding Index (CCI). The CCI is mapped through an explicit IF-THEN rule engine to four crowding categories (Low, Moderate, High, Extreme), each linked to a validated governance action package. A deterministic rule-based approach was chosen over probabilistic or machine-learning alternatives to ensure full decision traceability, which is a non-negotiable requirement for public-sector governance. System robustness was evaluated through structured scenario testing across 140 logic-coverage cases, assessed against four criteria: output consistency (100%), expert rule alignment (97.8%), decision traceability (100%), and processing efficiency (<1.15 seconds per scenario). The artifact successfully automates the mapping of site-level crowding status to discrete, auditable governance actions. The theoretical contribution lies in formalizing subjective management reasoning into a transparent, reproducible DSS that bridges sustainability science and institutional practice in high-pressure marine tourism environments.