Article Per Year (5 Year)
p-Index From 2021 - 2026
2.949
P-Index
This Author published in this journals
All Journal dCartesian: Jurnal Matematika dan Aplikasi Jurnal MIPA Jurnal Teknik Informatika Jurnal TIMES JOIV : International Journal on Informatics Visualization Knowledge Engineering and Data Science BAREKENG: Jurnal Ilmu Matematika dan Terapan ILKOM Jurnal Ilmiah Jurnal Ilmiah Behongang Journal of Applied Data Sciences Operations Research: International Conference Series JURNAL REKAYASA INFORMASI SWADHARMA (JRIS) Journal of Agriculture (JoA) Jurnal Ilmiah Sains Bulletin of Social Informatics Theory and Application d'Cartesian: Jurnal Matematika dan Aplikasi Jurnal Informatika dan Rekayasa Perangkat Lunak
Luther Latumakulita
Unknown Affiliation
Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Economics, Econometrics & Finance Electrical & Electronics Engineering Energy Engineering Library & Information Science Mathematics Mechanical Engineering Physics Social Sciences Transportation

Published : 36 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 36 Documents
Search

 1   2   3   4 
Subring dan Ideal pada Ring JR-2CN dan JR-3CN Julana Rarung; Mans Mananohas; Luther Latumakulita
d\'Cartesian: Jurnal Matematika dan Aplikasi Vol. 4 No. 1 (2015): Maret 2015
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.4.1.2015.8098

Abstract

Ring adalah himpunan dengan dua operasi biner dan memenuhi semua aksioma Ring. Adapun dua himpunan yang telah dibuktikan bahwa keduanya merupakan Ring yaitu, himpunan  pasangan terurut dari bilangan bulat baru JR – 2CN dan JR – 2CN.  Dalam tulisan ini, akan ditunjukkan beberapa Subring maupun Ideal pada Ring JR – 2CN dan JR – 2CN. Kata kunci : Ideal, JR – 2CN, JR – 2CN, Subring
Aplikasi Fuzzy Goal Programming (Studi Kasus: UD. Sinar Sakti Manado) Felliks Tampinongkol; Altien Rindengan; Luther Latumakulita
d\'Cartesian: Jurnal Matematika dan Aplikasi Vol. 4 No. 2 (2015): September 2015
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.4.2.2015.8650

Abstract

Pengoptimalan merupakan salah satu proses penentuan tinggi rendah-nya suatu pendapatan atau memaksimumkan keuntungan, meminimumkan tenaga kerja sesuai dengan yang diinginkan oleh pengambil keputusan. Penentuan solusi optimal sering kali dipakai oleh sebuah perusahaan atau organisasi yang bergerak dibidang pemasaran atau produksi untuk memperoleh informasi yang dibutuhkan. Tujuan dari penelitian ini adalah membuat user interface (UI) untuk menyelesaikan masalah fuzzy goal programming (FGP) dan penentuan hasil solusi optimal pada perusahaan UD. Sinar Sakti Manado. Data yang digunakan merupakan data hasil produksi perusahaan pada tahun 2014. Dari hasil yang diperoleh, jika pengambil keputusan perusahaan menginginkan pendapatan minimum Rp. 250.000.000 dan waktu kerja maximum 2400 jam, maka keuntungan yang akan diperoleh perusahaan sebesar Rp. 318.816.650318.816.650 dengan produk yang harus dihasilkan adalah 2 buah Dresoar uk.250, 28,7646 buah Dresoar uk.150, 2 buah Dresoar uk.100 dan 24,1966 set Sofa Laminating. Kata kunci : Fuzzy Goal Programming, Optimalisasi, User InterfaceCallSend SMSCall from mobileAdd to SkypeYou'll need Skype CreditFree via Skype
Model Antrian pada Sistem Pembayaran di Golden Pasar Swalayan Manado Marni Sumarno; Yohanes Langi; Luther Latumakulita
d\'Cartesian: Jurnal Matematika dan Aplikasi Vol. 4 No. 2 (2015): September 2015
Publisher : Sam Ratulangi University

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35799/dc.4.2.2015.9057

Abstract

Tujuan dari penelitian ini adalah untuk menganalisa model antrian pada sistem pembayaran di Golden Pasar Swalayan dalam hal jumlah kasir yang optimal.Penelitian ini dilakukan di Golden Pasar Swalayan selama 5 hari pukul 15.00 s/d 17.00 WITA.Metode penelitian yang digunakan adalah model antrian saluran ganda poisson dengan pola pelayanan eksponensial dan model tingkat aspirasi pada supermarket.Hasilnya menunjukkan bahwa pada waktu sibuk, dengan 7 kasir persentase kesibukan karyawan sebesar 94% dan waktu menunggu pelanggan 3.6 menit.Jika disimulasi dengan 8 kasir, persentase kesibukan karyawan sebesar 82% dan waktu menunggu pelanggan 1.8 menit. Kata kunci : Golden Pasar Swalayan Manado, Model Antrian Saluran Ganda, Sistem Antrian
Web-Based System for Medicinal Plants Identification Using Convolutional Neural Network Latumakulita, Luther; Mandagi, Franklin; Paat, Frangky; Tooy, Dedie; Pakasi, Sandra; Wantasen, Sofia; Pioh, Diane; Mamarimbing, Rinny; Polii, Bobby; Pongoh, Jantje; Pinaria, Arthur; Tenda, Edwin; Islam, Noorul
Bulletin of Social Informatics Theory and Application Vol. 6 No. 2 (2022)
Publisher : Association for Scientific Computing Electrical and Engineering

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31763/businta.v6i2.601

Abstract

Indonesia has a variety of medicinal plants that are efficacious for preventing or treating various diseases. Each region has unique medicinal plants, such as in North Sulawesi, there are many medicinal plants with local names of "Jarak" (Jatropha curcas), "Jarak Merah" (Jatropha multifida), "Miana" (Coleus Scutellarioide), and "Sesewanua" (Clerodendron Squmatum Vahl). This research applies the Convolutional Neural Network (CNN) method to identify the types of medicinal plants of North Sulawesi based on leaf images. Data was collected directly by taking photos of medicinal plant leaves and then using the augmentation process to increase the data. The first stage is conducting training and validation processes using 10-fold cross-validation, resulting in 10 classification models. Evaluation results show that the lowest validation accuracy of 98.4% was obtained from fold-4, and the highest was 100% from fold-2. The third stage was to run the testing process using new data. The results showed that the worst model produced a test accuracy of 80.91% while the best model produced an accuracy of 87.73% which means that the identification model is quite good and stable in classifying types of medicinal plants based on its leaf images. The final stage is to develop a web-based system to deploy the best model so people can use it in real-time
A Convolutional Neural Network-based Intelligence System for the Identification of Copra Maturity Levels Latumakulita, Luther Alexander; Paat, Frangky J; Budiman, Glenn; Tooy, Dedie; Koibur, Mayko Edison; Islam, Noorul
JOIV : International Journal on Informatics Visualization Vol 8, No 3 (2024)
Publisher : Society of Visual Informatics

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.62527/joiv.8.3.2574

Abstract

The North Sulawesi Province, widely recognized as the Coconut Waving Province owing to its substantial coconut tree population, primarily depends on copra production. This research presents a novel methodology for determining copra maturity levels by utilizing a Convolutional Neural Network (CNN) on digital photographs, classifying them into three distinct stages: raw, half-ripe, and ripe. By employing a rigorous 10-fold cross-validation technique, our models demonstrated remarkable performance. Notably, even the model with the lowest performance achieved a commendable accuracy of 87.78% during the training and validation phases. The model that exhibited the highest level of performance achieved a perfect accuracy rate of 100%. Moreover, when subjected to real-world testing situations using novel data, the model with the lowest performance exhibited a noteworthy accuracy of 83.34%. In contrast, the highest-performing model achieved a flawless accuracy of 100%. Based on the findings above, an online system has been built that leverages the most optimal model, facilitating the assessment of copra maturity in real-time. The prospects encompass the integration of this methodology into copra sorting machinery, thereby yielding advantages for both agricultural producers and industrial sectors. This research enhances copra quality control processes and promotes sustainability in the copra industry. Further research could explore refining the CNN model to accommodate a broader range of copra variations and investigating automation possibilities in copra production processes. These endeavors would advance the efficacy and applicability of copra maturity classification methods, fostering continued innovation in the industry.
Design Web-Based Information System of Tri Dharma Higher Education for Lecturer Anastasia, Lenshy Aprisilia; Pinontoan, Benny; Latumakulita, Luther Alexander
Operations Research: International Conference Series Vol. 3 No. 1 (2022): Operations Research International Conference Series (ORICS), March 2022
Publisher : Indonesian Operations Research Association (IORA)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47194/orics.v3i1.130

Abstract

Tri dharma of higher education consisting of education & teaching, research, and community service which must upheld by every lecturer at universities in Indonesia. Lecturer who has implemented the tri dharma of higher education need these tri dharma files for promotion and lecturer performance report to get allowance. It is important to have an online system to store those files. In this research we develop a web-based information system for processing lecturer tri dharma activities data using Rapid Application Development (RAD) Method which started from requirement planning, system design, and implementation.
Debtor Eligibility Prediction Using Deep Learning with Chatbot-Based Testing Noviania, Reski; Sela, Enny Itje; Latumakulita, Luther Alexander; Sentinuwo, Steven R.
Knowledge Engineering and Data Science Vol 7, No 2 (2024)
Publisher : Universitas Negeri Malang

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17977/um018v7i22024p128-138

Abstract

Predicting debtor eligibility is essential for effective risk management and minimizing bad credit risks. However, financial institutions face challenges such as imbalanced data, inefficient feature selection, and limited user accessibility. This study combines Recursive Feature Elimination (RFE) and Deep Learning (DL) to improve prediction accuracy and integrates a chatbot interface for user-friendly testing. RFE effectively identifies critical features, while the DL model achieves a validation accuracy of 97.62%, surpassing previous studies with less comprehensive methodologies. The chatbot's novel design not only ensures accessibility but also enhances user engagement through flexible input options, such as approximate values, enabling non-experts to interact seamlessly with the system. For financial institutions, this chatbot-based testing approach offers practical benefits by streamlining debtor evaluation processes, reducing dependency on manual assessments, and providing consistent, scalable, and efficient solutions for credit risk management. It allows institutions to handle inquiries outside business hours, ensuring a continuous service flow. Furthermore, the system’s flexibility supports better customer interaction, increasing trust and transparency. By combining advanced machine learning with accessible interfaces, this study offers a scalable solution to improve the precision and practicality of debtor eligibility assessments, making it a valuable tool for modern financial institutions.
Comparison of MobileNet and VGG16 CNN Architectures for Web-based Starfish Species Identification System Latumakulita, Luther Alexander; Paat, Frangky J.; Saroyo, Saroyo; Karim, Irwan; Astawa, I Nyoman Gede Arya; Sirait, Hasanuddin
Journal of Applied Data Sciences Vol 5, No 4: DECEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i4.456

Abstract

Bunaken Marine Park (BMP) is famous for its rich marine biodiversity. BMP is an asset for the marine tourism industry of the Manado city government, and the North Sulawesi Province of Indonesia needs to be strengthened. This research aims to build a web-based intelligent system using a convolutional neural network (CNN) to identify starfish species to initiate developing a media center marine biota identification system of BMP. Two CNN architectures, namely MobileNet and VGG16, were conducted to produce identification models. The first stage carried out a training process on 1800 starfish image data and then evaluated using the 5-fold cross-validation technique. Validation results show that MobileNet is superior to the VGG16 architecture by achieving validation accuracy of 100% for each fold while VGG16 produces validation accuracy in the range of 94% to 100%. On the other hand, in the second stage of model testing, it was found that VGG16 worked better than MobileNet in identifying 200 new data. The Best Model produced by VGG16 achieved testing accuracy of 100% while MobileNet produced 99.5%. However, stability analysis of the identification models produced by both architectures shows that MobileNet has relatively small loss values ranging from 0.00069325 to 0.00214802 as well as smaller standard deviation values of 0.27 compared to 0.61 produced by VGG16. These findings indicate MobileNet is more stable in carrying out identification work compared to VGG16 of, thus the best model provided by MobileNet is taken to deploy in the web platform which is created using the Python flask framework. The proposed system can be used to strengthen the marine tourism industry as a media center of educational marine biota using deep learning approaches.
SISTEM INFORMASI REE LAUNDRY BERBASIS WEBSITE MENGGUNAKAN METODE EXTREME PROGRAMMING Manarisip, Endrue Jehezkiel; Latumakulita, Luther Alexander; Ngangi, Stefano C.W.; Ketaren, Eliasta
Jurnal TIMES Vol 13 No 2 (2024): Jurnal TIMES
Publisher : STMIK TIME

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51351/jtm.13.2.2024779

Abstract

Kemajuan teknologi informasi mempermudah kehidupan sehari-hari dan meningkatkan efisiensi bisnis, khususnya dalam industri laundry. Teknologi informasi memungkinkan lebih banyak individu, termasuk perempuan dan ibu rumah tangga, untuk menciptakan peluang karier di rumah tanpa meninggalkan tanggung jawab keluarga. Industri laundry, yang menyediakan jasa cuci dan setrika, kini menjadi kebutuhan pokok dengan persaingan ketat yang mendorong inovasi seperti layanan antar jemput cucian. Penelitian ini bertujuan untuk mengembangkan sistem informasi berbasis web yang membantu pengelolaan data transaksi dan pelanggan bagi bisnis laundry, sekaligus memudahkan pelanggan dalam mengakses informasi dan memesan layanan secara efisien.
THE EFFECT OF ARABICA AND ROBUSTA COFFEE BLENDS ON CAFFEINE CONTENT, ACIDITY AND ORGANOLEPTIC PROPERTIES OF INSTANT COFFEE Langi, Tineke M; Paat, Frangky Jessy; Kusuma, Samuel D. A.; Oessoe, Yoakhim Y.E.; Liwu, Suzanne L.; Mamuaja, Christine F; Latumakulita, Luther A.; Tooy, Dedie; Rumambi, David P; Pinatik, Herry F; Mamarimbing, Rinny
Journal of Agriculture Vol. 2 No. 02 (2023): Research Articles, July 2023
Publisher : ITScience (Information Technology and Science)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/joa.v2i02.2806

Abstract

One of the types of coffee in Indonesia that can be processed into instant coffee is Gayo coffee. This research was conducted to find out the quality of instant coffee brewed in a mixture of arabica and robusta Gayo coffee. The method used in this research is a complete randomized design method (RAL) consisting of five treatments of a mixture of arabica coffee and robusta Gayo by making observations on acidity levels (pH), caffeine levels, and organoleptic of instant coffee brewing. The results showed that the mixed instant coffee types of Arabica and Robusta Gayo from each formulation produced different levels of acidity with a pH value of 5.55 – 6.43 and a different caffeine concentration with a value of 2.79% - 3.27% and qualified the quality requirements of the 2014 Indonesian National Standard (SNI) with a caffeine concentration value above a minimum of 2.5%. The brewing taste favored by the panelists was Gayo instant coffee, a mixture of 50% Arabica: 50% Robusta with a mild sour and bitter taste, a pH value of 5.94, and a caffeine content of 3.19%. The color and aroma of the coffee brewing favored by the panelists was Gayo instant coffee, a mixture of 70% arabica: 30% robusta with the quality of the brewing color of dark chocolate, the aroma of a little spice, the pH value of 5.55 , and the caffeine content of 2.79%.
Co-Authors Aji Prasetya Wibawa Altien Rindengan Altien Rindengan Alwin Melkie Sambul Ambarita, Yolanda Margareta Anastasia, Lenshy Aprisilia Andar Alwein Pinilas Arista Mandagi Arthur G. Pinaria Assa, Jan Rudolf Benny Pinontoan Bernard Bernard, Bernard Bobby Polii Budiman, Glenn Chriestie E. J. C. Montolalu Chriestie E. J. C. Montolalu Dedie Tooy Deiby Tineke Salaki Djoni Hatidja Eliasta Ketaren, Eliasta Enny Itje Sela Fajar Purnama Felliks Tampinongkol, Felliks Frangky J. Paat Gybert Saselah I Nyoman Gede Arya Astawa Islam, Noorul Jabari, Nida Jantje Pongoh Jevenston Lalenoh John Socrates Kekenusa Julana Rarung Julana Rarung, Julana Jullia Titaley Karim, Irwan Koibur, Mayko Edison Kusuma, Samuel D. A. Lapihu, Dodisutarma Lindsay Mokosuli Liwu, Suzanne L. Mairi, Vitrail Gloria Mamuaja, Christine F Manarisip, Endrue Jehezkiel Mandagi, Franklin Mans Mananohas Mans Mananohas, Mans Marni Sumarno Marni Sumarno, Marni Max R Kumaseh Miske Silangen Montolalu, Chriestie Ellyane Juliet Clara NELSON NAINGGOLAN NELSON NAINGGOLAN Ngangi, Stefano C.W. Ngangi, Stephano Caesar Wenston Noorul Islam Noviania, Reski Oessoe, Yoakhim Y.E. Paat, Frangky J Paat, Frangky Jessy Paat, Franky Pagewang, Yalon Bu'tu Pinatik, Herry F Pioh, Diane Raintung, Stephanie Marceline Rindengan, Altien J. Rinny Mamarimbing Rumambi, David P Salaky, Deiby Tineke Sandra Pakasi Sandy Laurentius Lumintang Sanriomi Sintaro Saroyo Saroyo Selvie Tumbelaka Sirait, Hasanuddin Sofia Wantasen Steven Ray Sentinuwo Sulu, Brian Sumual, Gery Josua Surahman, Ade Takaendengan, Mahardika Inra Tangkeallo, Sindy C. T. Teltje Koapaha Tenda, Edwin Tineke M. Langi Vederico Pitsalitz Sabandar Winsy Weku Winsy Weku Yohanes Langi Yohanes Langi