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Knowledge Management System Sharing Record Teknisi Berbasis Android Pada PT. CNC Part Teknika A. Yudi Permana; Ananto Tri Sasongko; Rita Purnamasari
Jurnal SIGMA Vol 13 No 2 (2022): Juni 2022
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Abstract Reports contain facts about news, information, notifications, and forms of activities relating to accountability. Job reports that have not been properly documented are also an obstacle for the company when a technician resigns, making it difficult to distribute the knowledge possessed by the old technician to the new technician. Knowledge Management System is one way to identify, select, disseminate and disseminate important information and expertise in an organization as an effort to develop productivity and work performance so as to increase the competitiveness of the organization. The development of information systems is fast, accurate and up to date available in the plat form, such as Android. In this case, PT. CNC Part Teknika which is engaged in the field of General Trading and Service, still uses a manual system for making information job report. One example of the report method they used paper for media report. However, this reporting method is easily lost and damaged. This research aimed to design application knowledge management system job report based on android system. In designing this application using the XP (Extreme programing) and UML (Unified Modeling Language) methods. This is expected to documenting the knowledge of technicians in handling service and help facilitate the process of making reports is up to date for technician at PT. CNC Part Teknika. This application is useful for technician workers of PT. CNC Part Teknika using a database that can be used easily and quickly. Keywords: Reports, Knowledge Management System, android, General Trading and Service, UML.

Klasifikasi Barang Paling Laku (Pareto) Indomaret Untung Suropati 35 (T3m1) Menggunakan Rapidminer Dengan Metode Naive Bayes Edy Widodo; Ananto Tri Sasongko; Antika Zahrotul Kamalia
Jurnal SIGMA Vol 13 No 4 (2022): Desember 2022
Publisher : Teknik Informatika, Universitas Pelita Bangsa

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Penelitian ini dilatarbelakangi oleh banyaknya barang paling laku. Salah satunya pengiriman barang dari pusat distribusi ke store. Masalah utama dalam penelitian ini, adalah: banyaknya kiriman barang yang kurang laku membuat area gudang tidak bisa menahan barang yang membuat barang tersebut menjadi over stok. Pendekatan yang digunakan dalam penelitian ini adalah pendekatan 2 jenis data yaitu kualitatif dan kuantitatif dengan metode klasifikasi naive bayes. Teknik pengambilan sampel menggunakan teknik data penjualan dengan total sampel 1.173 item. Teknik pengumpulan data dengan obervasi, wawancara, dan dokumentasi. Teknik analisis penelitian data yang digunakan adalah klasifikasi. Dari dokumen yang diperoleh hasilnya bahwa klasifikasi barang paling laku (pareto) Indomaret Untung Suropati 35 (T3M1) menggunakan Tools Rapidminer dengan Metode Naive Bayes. Adapun yang diperoleh dapat memprediksi barang yang benar-benar dibutuhkan dan dahulukan dalam pengiriman dari pusat distribusi barang. Tujuan penelitian menggunakan Tools Rapidminer untuk menghasilkan data-data yang lebih akurat dalam proses penjualan barang retail dengan konsumen itu sendiri seperti pedagang retail, grosir, Pareto dan supermarket. Penelitian berbentuk studi kasus dengan metode penelitian Neive Bayes. Penelitian Klasifikasi Penjualan Barang Paling Laku (Pareto) di Indomaret Untung Suropati 35 (T3M1) menggunakan Data Mining ini memperlihatkan proses penjualan barang yang paling laku memiliki verifikasi yang akurat mengenai sistem pendataan barang, stok barang, ketersediaan barang, FIFO (First In First Out),FEFO (First End First Out) dengan tujuan mempermudah karyawan dalam melakukan transaksi proses dan penerimaan barang dari supplier dan dari Pusat DC (Distribution Center) ke toko. Hasil penelitian klasifikasi barang paling laku (pareto) Indomaret Untung Suropati 35 (T3M1) menggunakan Tools Rapidminer dengan Metode Naive Bayes memiliki nilai akurasi 88,50%, precision 97,92%, recall 81,74%. Dari hasil validasi penghitungan metode klasifikasi Naive Bayes dengan Tools Rapidminer mampu memberikan penjabaran secara signifikan dengan nilai akurasi yang baik dan berpengaruh pada prediksi penerimaan barang yang sesuai dengan permintaan dan kebutuhan konsumen. Kata kunci : Tools Rapidminer, Retail, Konsumen, Distributor, Klasifikasi, Metode Neive Bayes, Pareto

Studi Literatur Konsep dan Implementasi Sains Data untuk Memaksimalkan Kinerja Industri Manufaktur Ananto Tri Sasongko
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 2 (2023): April 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i2.778

The manufacturing industry is currently faced with increasingly complex and diverse challenges. To address this challenge, companies need an approach based on data science to improve production performance and efficiency. This paper proposes the concept and implementation of data science in the manufacturing industry. Several important concepts in data science, including predictive analysis, prescriptive analysis, and optimization, are introduced. It also highlights the algorithms and data analysis techniques used to maximize production performance and reduce costs. Implementing data science in the manufacturing industry involves steps such as data collection, data processing, data analysis, and decision-making. Several use cases of data science in the manufacturing industry, including predictive analytics to forecast machine failures, prescriptive analysis to increase productivity, and optimization to optimize production schedules, are discussed here. The results of this scientific paper show that implementing data science in the manufacturing industry can significantly improve production performance and efficiency. This scientific paper provides useful insights for practitioners, researchers, and policymakers in the manufacturing industry who are interested in applying data science to production processes

Penerapan Naïve Bayes Classifier, Support Vector Machine, dan Decision Tree untuk Meningkatkan Deteksi Ancaman Keamanan Jaringan Ahmad Zy; Ananto Tri Sasongko; Antika Zahrotul Kamalia
KLIK: Kajian Ilmiah Informatika dan Komputer Vol. 4 No. 1 (2023): Agustus 2023
Publisher : STMIK Budi Darma

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30865/klik.v4i1.1134

This research aims to implement three machine learning algorithms, namely Naïve Bayes Classifier, Support Vector Machine (SVM), and Decision Tree, to enhance network security threat detection. The study utilizes data from multiple sources to train the machine learning models and evaluate their performance in detecting network security threats such as malware, ransomware, and spyware. The research results indicate that all three machine learning algorithms can improve the effectiveness of network security threat detection, surpassing conventional methods in terms of accuracy. Decision Tree yields the best results with a precision of 0.98, , followed by SVM with a precision of 90%, While Naïve Bayes Classifier a precision of 0.86.

HARNESSING THE POWER OF PROTOTYPING METHOD FOR ENGAGING RESPONSIVE WEB PROFILES Ananto Tri Sasongko; Sunita Dasman
JIKO (Jurnal Informatika dan Komputer) Vol 6, No 2 (2023): JIKO (Jurnal Informatika dan Komputer)
Publisher : Universitas Khairun

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33387/jiko.v6i2.6399

This research discusses the implementation of the prototyping method in enhancing institutional visibility through the development of responsive web profiles. Institutional visibility is key to achieving success and sustainable growth in today's digital era. Developing a responsive web profile increases visibility by providing optimal user experience across various devices and screen resolutions effectively. The prototyping method was the primary approach in developing this responsive web profile. It allows developers to create initial models that can be tested and evaluated before the full development of the web profile. The research explains the steps in developing a responsive web profile using prototyping. The results show that this method offers an efficient and practical approach to creating a responsive web profile, ensuring user satisfaction, and meeting the increasing expectations of users. Therefore, institutions should consider applying this prototyping method to strengthen their visibility through innovative, responsive web profiles. Responsive web profiles enable institutions to reach and engage their target audience through different devices, enhancing user engagement and providing consistent user experiences. The result shows that prototyping enhances institutional web profiles, improves user experience, and effectively increases visibility with high satisfaction, with an average of 83.2.

ANALISIS PREDIKSI GILINGAN PLASTIK TERLARIS MENGGUNAKAN ALGORITMA K-NEAREST NEIGHBOR DI CV MENEMBUS BATAS Amanda Pratiwi; Ananto Tri Sasongko; Dendy K. Pramudito
Jurnal Informatika Teknologi dan Sains (Jinteks) Vol 5 No 3 (2023): EDISI 17
Publisher : Program Studi Informatika Universitas Teknologi Sumbawa

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51401/jinteks.v5i3.3323

The phenomenon of abundant plastic waste is a global problem that has a broad impact, including on the recycling industry such as CV Breaking the Limit. The main challenge facing companies is the difficulty of predicting which products will be most in demand by the market. However, through this research, using historical sales data from the period April 2022 to April 2023, managed to identify ACR Mill products as the best-selling products that are most in demand by consumers. The application of the K-Nearest Neighbor algorithm method in sales prediction helps companies to optimize production, manage stocks, and allocate resources more efficiently. The results showed that the K-Nearest Neighbor algorithm rovides very accurate predictions, with accuracy, recall, and precision values reaching 1.0 in product classification, so it can be relied on in supporting the sustainability of the plastic recycling business amid global challenges related to plastic waste.

Sistem Otomasi Lazy Minting NFT di Marketplace Rarible pada Blockchain Ethereum Menggunakan Autoit Alfin Abdilah; Putri Anggun Sari; Ananto Tri Sasongko
Jurnal Teknologi Dan Sistem Informasi Bisnis Vol 5 No 3 (2023): July 2023
Publisher : Prodi Sistem Informasi Universitas Dharma Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47233/jteksis.v5i3.915

This research is motivated by the difficulty of the Lazy Minting NFT process in the Rarible Marketplace. The NFT Lazy Mining process on the Rarible Marketplace can be said to be inefficient because the process still has to be done one by one. Not to mention other problems that arise such as wrong numbering and typing errors or typos. This resulted in the Lazy Minting NFT process on the Rarible Marketplace becoming less effective. This problem will make it difficult for NFT creators if they have a large number of digital works. Therefore, this research aims to build an automated NFT Lazy minting system on the Rarible Marketplace on the Ethereum Blockchain using AutoIt, so that the NFT Lazy minting process on the Rarible Marketplace becomes more effective and efficient. The method used in the development of the Lazy Minting NFT automation system in the Rarible Marketplace is Agile Development. This research resulted in a Lazy minting NFT automation system on the Rarible Marketplace on the Ethereum Blockchain. The conclusion of this study is that the application of this system is able to make the NFT Lazy mining process on the Rarible Marketplace more effective and efficient. This can make it easier for NFT creators in their NFT sales process.

Integrasi Sensor DHT11 dan PIR dalam Sistem Otomatisasi Suhu dan Deteksi Gerakan dalam Ruangan Menggunakan Mikrokontroler Arduino Nano: Integration of DHT11 and PIR Sensors in Indoor Temperature Automation and Motion Detection System Using Arduino Nano Microcontroller Pratifi, Via Khusnul; Sasongko, Ananto Tri; Afandi, Dedi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1490

Penelitian ini bertujuan untuk merancang dan membangun sistem kontrol suhu dan deteksi gerakan pada ruangan dengan memanfaatkan Arduino Nano yang dilengkapi dengan sensor DHT11 dan sensor PIR. Jenis penelitian yang digunakan adalah penelitian observatif eksperimental. Penelitian observatif eksperimental dimulai dengan menentukan alat dan bahan yang diperlukan, termasuk perangkat lunak seperti Arduino IDE, Fritzing, Draw.io, dan Microsoft Word. Perangkat keras yang digunakan meliputi laptop HP dengan prosesor AMD Ryzen 5 5500U, Arduino Nano, sensor PIR, sensor DHT11, komponen relay, LCD, Buzzer, LED, power supply 12 volt, kabel, dan kipas angin Maspion F-15 DA. Alat pendukung seperti cutter, obeng, solder, tang potong, dan bor listrik digunakan untuk perakitan komponen elektronika agar sistem dapat berfungsi sesuai rencana. Metode pengumpulan data dalam penelitian ini meliputi observasi langsung terhadap kondisi ruang kelas di SMA N 1 Petanahan, serta studi pustaka untuk mendapatkan landasan teori dan informasi pendukung tentang sistem kontrol suhu, sensor, dan komponen yang digunakan. Hasil penelitian menunjukan bahwa sistem kontrol suhu dan deteksi gerakan telah melewati pengujian yang memuaskan. Sistem mampu menjaga suhu ruangan pada rentang yang diinginkan dengan efektif, sementara sensor DHT11 menunjukkan akurasi yang tinggi dalam pengukuran suhu, dengan perbedaan yang sangat kecil antara nilai yang terbaca dan suhu aktual ruangan.

Integrasi Sensor DHT11 dan PIR dalam Sistem Otomatisasi Suhu dan Deteksi Gerakan dalam Ruangan Menggunakan Mikrokontroler Arduino Nano: Integration of DHT11 and PIR Sensors in Indoor Temperature Automation and Motion Detection System Using Arduino Nano Microcontroller Pratifi, Via Khusnul; Sasongko, Ananto Tri; Afandi, Dedi
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 3 (2024): MALCOM July 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i3.1490

Penelitian ini bertujuan untuk merancang dan membangun sistem kontrol suhu dan deteksi gerakan pada ruangan dengan memanfaatkan Arduino Nano yang dilengkapi dengan sensor DHT11 dan sensor PIR. Jenis penelitian yang digunakan adalah penelitian observatif eksperimental. Penelitian observatif eksperimental dimulai dengan menentukan alat dan bahan yang diperlukan, termasuk perangkat lunak seperti Arduino IDE, Fritzing, Draw.io, dan Microsoft Word. Perangkat keras yang digunakan meliputi laptop HP dengan prosesor AMD Ryzen 5 5500U, Arduino Nano, sensor PIR, sensor DHT11, komponen relay, LCD, Buzzer, LED, power supply 12 volt, kabel, dan kipas angin Maspion F-15 DA. Alat pendukung seperti cutter, obeng, solder, tang potong, dan bor listrik digunakan untuk perakitan komponen elektronika agar sistem dapat berfungsi sesuai rencana. Metode pengumpulan data dalam penelitian ini meliputi observasi langsung terhadap kondisi ruang kelas di SMA N 1 Petanahan, serta studi pustaka untuk mendapatkan landasan teori dan informasi pendukung tentang sistem kontrol suhu, sensor, dan komponen yang digunakan. Hasil penelitian menunjukan bahwa sistem kontrol suhu dan deteksi gerakan telah melewati pengujian yang memuaskan. Sistem mampu menjaga suhu ruangan pada rentang yang diinginkan dengan efektif, sementara sensor DHT11 menunjukkan akurasi yang tinggi dalam pengukuran suhu, dengan perbedaan yang sangat kecil antara nilai yang terbaca dan suhu aktual ruangan.

Optimasi Decision Tree Menggunakan Particle Swarm Optimization (PSO) pada Risiko Kredit KMG Bank DKI: Optimization of Decision Tree Using Particle Swarm Optimization (PSO) for Credit Risk of KMG Bank DKI Putry, Jwasky Budy Eswa; Sasongko, Ananto Tri; Hadikristanto, Wahyu
MALCOM: Indonesian Journal of Machine Learning and Computer Science Vol. 4 No. 4 (2024): MALCOM October 2024
Publisher : Institut Riset dan Publikasi Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.57152/malcom.v4i4.1521

Pada dunia perbankan prediksi risiko kredit merupakan aspek penting yang menentukan keberhasilan dalam pengelolaan kredit. Penelitian ini bertujuan untuk meningkatkan akurasi prediksi risiko kredit Kredit Multiguna (KMG) di Bank DKI dengan menggunakan metode Particle Swarm Optimization (PSO). Dalam konteks ini, PSO digunakan untuk mengoptimalkan dalam menemukan kombinasi parameter terbaik yang dapat meningkatkan performa model prediksi risiko kredit. Penelitian menunjukkan bahwa penggunaan Particle Swarm optimization (PSO) ini meningkatkan akurasi prediksi risiko kredit secara signifikan. Dengan menggunakan Particle Swarm optimization (PSO) menghasilkan akurasi prediksi mencapai 99,13%. Sebaliknya , tanpa optimasi PSO, akurasi yang diperoleh dari Decision Tree hanya sebesar 97,83 %. Hal ini membuktikan bahwa PSO mampu meningkatkan akurasi prediksi risiko kredit secara signifikan. Dengan demikian, Bank DKI dapat mengambil keputusan yang lebih tepat dalam pemberian kredit KMG, yang pada akhirnya dapat mengurangi tingkat kredit macet dan meningkatkan stabilitas finansial bank.

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