Johanes Latupapua
Pasar Minggu, Jakarta Raya, Indonesia
2 rb pengikut
500+ koneksi
Tentang
A strong experiences data specialist, an IT supervisor who passionate in data science…
Aktivitas
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Let’s not forget that Data Lifecycle in 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 starts with 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴…
Let’s not forget that Data Lifecycle in 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 starts with 𝗗𝗮𝘁𝗮 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿𝗶𝗻𝗴…
Disukai oleh Johanes Latupapua
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Medtronic Indonesia is #hiring for a Sales Representative to join our Neurosciences team! Be part of a team that #EngineerTheExtraordinary, as we…
Medtronic Indonesia is #hiring for a Sales Representative to join our Neurosciences team! Be part of a team that #EngineerTheExtraordinary, as we…
Disukai oleh Johanes Latupapua
Pengalaman
Pendidikan
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BINUS UNIVERSITY GRADUATE PROGRAM
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Research thesis at deep learning domain that evaluated performance of Convolutional Neural Networks and optimizers on wildlife animal classification.
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Lisensi dan Sertifikasi
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Introduction Deep Learning with OpenCV
LinkedIn
DiterbitkanID Kredensial AQIjq7d7ZJvRPkY6UgqFr_dS59xm -
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Azure Spark Databricks Essential Training
LinkedIn
DiterbitkanID Kredensial AfXIeHYPtVApF8P-4ULzhprl6ZdL -
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Scalable Machine Learning on Big Data using Apache Spark
Coursera
DiterbitkanID Kredensial ZFACCP3BRRVA -
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Pengalaman Sukarela
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English Translator
Wahana Visi Indonesia
- 1 bulan
Social Services
English translator in one day Sponsor visit from HKG
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Co Facilitator
Markoding
- 1 bulan
Education
One-day Co facilitator at Markoding Innovation Challenge
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Data Super Mentor
Dealls – Jobs & Mentoring (YC W22)
- 2 bulan
Social Services
Mentored one student that has a strong passionate to have a career in data analytics!
Penerbitan
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Performance Evaluation of Convolutional Neural Networks and Its Optimizers on Wildlife Animal Classification
International Journal of Advanced Trends in Computer Science and Engineering
Selecting optimizer and hyperparamater settings are able to affect accuracy achieved significantly. Selected values are generally performed by trial and error. This study evaluates performance of Convolutional Neural Network (CNN) and two optimizers combined with changes in 6 learning rate values. Researched optimizers are Adaptive Moment Estimation and RMS Prop in wildlife animal classification domain. Other hyperparameter values are set same. The architectures are implemented on DenseNet 121,…
Selecting optimizer and hyperparamater settings are able to affect accuracy achieved significantly. Selected values are generally performed by trial and error. This study evaluates performance of Convolutional Neural Network (CNN) and two optimizers combined with changes in 6 learning rate values. Researched optimizers are Adaptive Moment Estimation and RMS Prop in wildlife animal classification domain. Other hyperparameter values are set same. The architectures are implemented on DenseNet 121, ResNet 50 and AlexNet. This study recommends best value of learning rate for training process which will be conducted with conditions similar to this study and provide other insights during study. The images in this study used wild animals with various positions from front side, back side and some part of bodies that describe real condition. In contradiction to other studies which generally use images in the hundreds of thousands to millions, the number of images used is 47, 841 taken from the Serengeti Season 1 Snapshot with imbalance conditions. Obtained result with the highest recall reached 79%.
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Business Intelligence for Employment Classification in Jakarta Government Data
2019 International Conference on ICT for Smart Society (ICISS)
As an open data which are provided by government, public can access and assist government to analyze and look for solution to social problems among communities. Jakarta Open Data shares employment data that is able to be starting point into analysis or recommendation by building data warehouse. OLTP data suggested for the data warehouse is the existing employment type based transactions, which will be used to analyze employment and unemployment rate in each region. The proposed method to design…
As an open data which are provided by government, public can access and assist government to analyze and look for solution to social problems among communities. Jakarta Open Data shares employment data that is able to be starting point into analysis or recommendation by building data warehouse. OLTP data suggested for the data warehouse is the existing employment type based transactions, which will be used to analyze employment and unemployment rate in each region. The proposed method to design the data warehouse is using 9-steps from Kimball. The selected process for the data warehouse is calculating number of employment type, with dimensions of region, education type, sex and employment type. Region has 3 level area in sub-district and village. The result of process and analysis will be provided into certain reports and dashboard. Dashboard will be very helpful directly to provide high level data for all stakeholder or decision maker.
Penulis lainnya -
Kursus
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Dphi Deep Learning Foundation Bootcamp
ba1581b4-ef02-4e5e-b6b4-4
Proyek
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Collection Score
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Objective of the project is to creating score for customer who has debt or arrears according to payment history information, follow up to customer activities. The model will help collection staff, collection head to monitor collection process. My role was assisting to cleansing raw data from various resources, identified data could having interconnection and relevancy.
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Credit Risk Scoring Model
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This model was created for customer scoring model of new customer when she/he proposed new motorcycle credit loan that was composed from 21 features of customer profiles on its initial version. Model has been scaled into production, officially used by credit analysts on April 2021. Later model grows up with 2-3 features added and separated into motorcycle and car loan.
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Loan Prediction by comparing 5 algorithms
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I tried to create simple loan prediction by comparing 5 algorithms with Kaggle dataset. There are 13 features with final decision is loan will be approved or not. Original idea was taken from Ajaymanwani's effort.
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Predict Banknotes whether authentic or not
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Simple Classification using Random Forest, Logistic Regression and SVM This is simple classification to predict whether banknotes are authentic or not. It was part of Dphi DL Bootcamps assignment which used one of popular datasets, UCI Machine Learning Repository. Some ideas comes from learning at bootcamps and other open sources. By using this dataset, I compared result of Random Forest and SVM. Happy learning all and keep growing!
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Performance Evaluation of CNN and Optimizer on Wildlife Animal Classification
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This is my research thesis to complete my education as Master of Computer Science at deep learning domain. The motivation of this study is to search best learning rate to achieve highest accuracy/recall in wildlife animal classification while using 3 state of the art CNN model and Adam/RMSProp optimizer.
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Business Intelligence for Employment Classification in Jakarta Government Data
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This is team paper work based on our assignment at Data Warehouse class. I worked with Kristian and Ritchie, my colleagues using Pentaho to extract, transform, load and analyze one citizenship dataset from Jakarta Open Data (https://data.jakarta.go.id/dataset). Two supervisors, our lecturers then proposed to an International conference and approved to be published there.
Kreator lainnya -
Bahasa
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English
Tingkat profesional mahir
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Indonesian
Tingkat fasih atau penutur asli
Organisasi
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Jakarta Machine Learning
Member
- Saat iniJakarta Machine Learning (JML) is a Jakarta-based nonprofit community, dedicated to connect Indonesian talents in Machine Learning from all over the world. We also have a mission to develop talent's capabilities in Machine Learning through Workshops and Focus Group Discussion. Our registered members ranging from Data Scientist, Consultant, Data Analyst, Business Analytics, Software Engineer, IT Lecturers, IT Manager and University students.
Aktivitas lainnya oleh Johanes
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🤷♀️ Evaluating RAG pipelines is crucial but often overlooked. Before adding fancy features, ensure your evaluation setup is solid. 😎 I've put…
🤷♀️ Evaluating RAG pipelines is crucial but often overlooked. Before adding fancy features, ensure your evaluation setup is solid. 😎 I've put…
Disukai oleh Johanes Latupapua
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🤖 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀: 𝗔𝗹𝗹 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄 👇 As I’ve said many times, I’m expecting from LLM’s much…
🤖 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀: 𝗔𝗹𝗹 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗸𝗻𝗼𝘄 👇 As I’ve said many times, I’m expecting from LLM’s much…
Disukai oleh Johanes Latupapua
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What is a 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲? With the rise of Foundational Models, Vector Databases skyrocketed in popularity. The truth is that a…
What is a 𝗩𝗲𝗰𝘁𝗼𝗿 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲? With the rise of Foundational Models, Vector Databases skyrocketed in popularity. The truth is that a…
Disukai oleh Johanes Latupapua
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Building a 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗴𝗿𝗮𝗱𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗯𝗮𝘀𝗲𝗱 𝗟𝗟𝗠 𝘀𝘆𝘀𝘁𝗲𝗺 is…
Building a 𝗽𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝗴𝗿𝗮𝗱𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻 (𝗥𝗔𝗚) 𝗯𝗮𝘀𝗲𝗱 𝗟𝗟𝗠 𝘀𝘆𝘀𝘁𝗲𝗺 is…
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Joint Monitoring Bayan Peduli and doctorSHARE; the Construction of New Floating/Ship Hospital *[Tanjung Bira, South Sulawesi]* – PT Bayan Resources…
Joint Monitoring Bayan Peduli and doctorSHARE; the Construction of New Floating/Ship Hospital *[Tanjung Bira, South Sulawesi]* – PT Bayan Resources…
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KORIKA Hammam Riza Welcoming Address Speech Meidy Fitranto Panel Discussion: Embarking on the journey of Digital Transformation The market size in…
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Powerful Network Kemarin saya mendapat kunjungan dari seorang teman yang saya kenal setahun belakangan ini. Pak Rivaldy Varianto S. kebetulan sedang…
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Advanced #RAG aims to address the limitations of Naive RAG. Advanced RAG uses more sophisticated LLMs like Claude 3 and AI frameworks and…
Advanced #RAG aims to address the limitations of Naive RAG. Advanced RAG uses more sophisticated LLMs like Claude 3 and AI frameworks and…
Disukai oleh Johanes Latupapua
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