Garuda - Garba Rujukan Digital

IMPLEMENTASI APLIKASI PENGENALAN SOP PENGOLAHAN LIMBAH INDUSTRI PT X BERBASIS ANDROID DEROSARI, ROSALINDA MENTIGASA DEROSARI; Handoko, Koko
Computer Science and Industrial Engineering Vol 10 No 1 (2024): Comasie
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v10i1.8140

B3 medical waste management raises specific considerations and can provide a major problem if not handled effectively. As a result, PT. Desa Air Cargo Batam is on hand to undertake the processing, and the company already holds a B3 industrial waste processing authorization. To meet the company's vision and objective, the correct Standard Operating Procedure (SOP) must be used to reduce negative affects on the environment and society. However, in these companies, awareness and comprehension of the Standard Operating Procedure (SOP) for the handling of B3 medical waste remains a concern that must be addressed. The Software Development Life Cycle (SDLC) research technique was utilized for software development, and application development in this study was done with Adobe Illustrator, Visual Studio Code, and Flutter. The resulting application is compatible with Android smartphones running at least version 8.0 (Oreo). The waterfall method is applied in this scenario. The findings of black-box testing and functional testing reveal that the application performed as predicted, and the results of this application questionnaire are in the very good category, with no problems when operating

IMPLEMENTASI FINITE STATE MACHINE DALAM GAME EDUKASI BAHASA JEPANG Saputra, Juan; Handoko, Koko
Computer Science and Industrial Engineering Vol 11 No 2 (2024): Comasie Vol 11 No 2
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v11i2.9084

The development of technology enables people to immerse themselves in Japanese culture, encompassing aspects such as fashion, culinary, arts, and language. This influence has sparked a rapid increase in Indonesian interest to learn Japanese. However, it is undeniable that few schools include Japanese language courses in their curriculums, this circumstances forces students to seek alternative learning sources such as private tutoring or courses. Moreover, traditional learning methods often leave students feeling bored dan exhausted, particularly through textbooks, thus demanding more engaging and relaxed learning approaches. Solas School of Languages, located in Batam, offers various language courses including Japanese. This study aims to implement the finite state machine method into an educational Japanese language visual novel game for Android, designed using Ren’Py game engine. This method will be applied to alter NPC character expressions within the game. The research output includes an educational Japanese language game featuring implemented finite state machines, the appearance of its interface, and data collected directly from students learning Japanese at Solas School of Languages. Thus, it is hoped that this research will demonstrate the effectiveness of game-based learning methods in making studying more engaging and assisting teachers in educating their students outside classroom.

ANALISIS PERBANDINGAN KINERJA ALGORITMA MACHINE LEARNING BERBASIS FEATURE SELECTION DALAM DETEKSI SERANGAN BOTNET Khoo, Rio; Handoko, Koko
Computer Science and Industrial Engineering Vol 12 No 2 (2025): Comasie Vol 12 No 2
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v12i2.9778

Internet has experienced significant development. Increasing devices connected to internet makes security against cyber attacks a critical issue, thus creates opportunities for cyber attackers, one form of those attack is botnets. In Indonesia, Botnets is the highest traffic anomalies in 2022 by BSSN. High number of attacks because detecting botnet can be challenging, difficulty of detecting attacks and low level of detection accuracy means that normal data sometimes considered an attack, so choosing method that can handle this is very important. Machine learning algorithms are able to study network data traffic and identify suspicious activity, this makes machine learning an effective method. Machine learning based on feature selection has an accuracy of above 90% in detecting DDoS attacks on datasets and machine learning algorithms are also able to detect attack data and normal data. Thus, in this research machine learning algorithms such as K-Nearest Neighbors, Support Vector Machine and Naive Bayes will be applied to dataset containing botnet and normal data to explore how machine learning algorithms can effectively detect botnet attack patterns and normal data. This research compares the performance of commonly used machine learning algorithms to find which one effective for detecting botnet attacks in existing datasets.

DATA MINING ALGORITMA APRIORI MENENTUKAN PEMBELIAN MATERIAL KONSTRUKSI BANGUNAN Simanjuntak, Pastima; Handoko, Koko; Elisa, Erlin; Suharyanto, Cosmas Eko
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 2 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

In Indonesia, several different types of construction materials are extensively utilized, particularly for houses or structures and other infrastructure sectors. Building materials might be constructed of metal/iron, wood, concrete, or reinforced concrete. Houses, buildings, or all facilities, equipment, or infrastructure such as bridges, road construction, and telecommunications facilities are commonly referred to as structures. Material is extremely significant in deciding project costs. Due to a lack of effective planning and control during the construction implementation stage, the usage of materials in the field frequently results in huge volumes of material remaining, hence measures to limit material waste are critical to implement. The goal of this research is to choose building materials. This study uses a data mining technique with an a priori algorithm and the results of this study can be utilized to see consumer selection patterns to boost product sales, as well as the subsequent decisions to enhance product sales, as well as see the resulting decision to assist in the selection of building construction materials in the City of Batam.

DATA MINING ALGORITMA APRIORI MENENTUKAN PEMBELIAN MATERIAL KONSTRUKSI BANGUNAN Simanjuntak, Pastima; Handoko, Koko; Elisa, Erlin; Suharyanto, Cosmas Eko
Jurnal Teknik Informatika UNIKA Santo Thomas Vol 8 No. 2 : Tahun 2023
Publisher : LPPM UNIKA Santo Thomas

Show Abstract | Download Original | Original Source | Check in Google Scholar

In Indonesia, several different types of construction materials are extensively utilized, particularly for houses or structures and other infrastructure sectors. Building materials might be constructed of metal/iron, wood, concrete, or reinforced concrete. Houses, buildings, or all facilities, equipment, or infrastructure such as bridges, road construction, and telecommunications facilities are commonly referred to as structures. Material is extremely significant in deciding project costs. Due to a lack of effective planning and control during the construction implementation stage, the usage of materials in the field frequently results in huge volumes of material remaining, hence measures to limit material waste are critical to implement. The goal of this research is to choose building materials. This study uses a data mining technique with an a priori algorithm and the results of this study can be utilized to see consumer selection patterns to boost product sales, as well as the subsequent decisions to enhance product sales, as well as see the resulting decision to assist in the selection of building construction materials in the City of Batam.

DISPLAYING SOCIAL MEDIA ADS DI PKBM SAHABAT CENDIKIA Handoko, Koko; Simanjuntak, Pastima; Elisa, Erlin; Zetli, Sri
PUAN INDONESIA Vol. 7 No. 1 (2025): Jurnal PUAN Indonesia Vol. 7 No. 1 Juli 2025
Publisher : ASOSIASI IDEBAHASA KEPRI

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.37296/jpi.v7i1.393

The purpose of this Community Service is to see how PKBM Sahabat Cendikia uses social media advertising to increase the visibility of the institution and increase community participation in non-formal education programs. In today's internet era, using social media is one of the effective ways to reach a larger audience, especially the younger generation who are active on websites such as Facebook and Instagram. This service uses a qualitative descriptive approach and data collection through documentation, interviews, and observations. The service shows that displaying social media ads increases the number of students and improves the reputation of the institution in the community. Visual ad design, frequency of display, and audience segmentation are important components that determine the effectiveness of a digital campaign. These findings are expected to be a reference for other non-formal educational institutions to use social media wisely to promote themselves.

Implementasi FP-Growth untuk Analisis Pola Pembelian Produk Elektronik pada Tokopedia Handoko, Koko; Darmansah, Darmansah; Adhiatma, Novri
Jurnal Pendidikan Sains dan Komputer Vol. 5 No. 02 (2025): Artikel Riset Oktober 2025
Publisher : Information Technology and Science (ITScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47709/jpsk.v5i02.6963

Kemajuan teknologi informasi, e-commerce Indonesia berkembang secara pesat, dengan Tokopedia menjadi salah satu situs belanja online terbesar. Dengan menggunakan algoritma FP-Growth, penelitian ini akan menganalisis pola pembelian produk elektronik di Tokopedia. Tahapan meliputi proses preprocessing data, pembentukan itemset, dan ekstraksi aturan asosiasi berdasarkan nilai minimum support dan confidence. Data yang digunakan terdiri dari 5.000 data transaksi yang dilakukan oleh pelanggan dengan berbagai jenis elektronik, seperti smartphone, laptop, charger, powerbank, earphone, mouse, dan flashdisk. Penelitian dilakukan dengan parameter minimum support yaitu 20% dan minimum confidence 50%. Hasil penelitian ini menunjukkan beberapa aturan asosiasi yang signifikan, seperti konsumen yang membeli smartphone cenderung juga membeli earphone (support 25%, confidence 65%). Konsumen membeli Mouse dan juga membeli Laptop (support 21%, confidence 70%). Konsumen membeli Flashdisk juga terkait dengan membeli Laptop (support 20%, confidence 60%). Konsumen membeli Powerbank juga membeli Charger (support 24%, confidence 55%). Kemudian pola dengan nilai lift tertinggi adalah Smartphone dan Earphone (1,25) serta Powerbank dan Charger (1,22), yang menunjukkan bahwa kombinasi ini muncul bersama jauh lebih sering daripada jika pembelian dilakukan secara acak. Oleh karena itu, penelitian ini menemukan bahwa algoritma FP-Growth dapat dengan mudah menemukan pola pembelian produk elektronik dan dapat digunakan sebagai dasar pengambilan keputusan berbasis data dalam industri e-commerce.

Digital Marketing Dalam Kewirausahaan Pada Masa Pandemi Covid 19 Simanjuntak, Pastima; Handoko, Koko; Elisa, Erlin; Eko Suharyanto, Cosmas
Jurdimas (Jurnal Pengabdian Kepada Masyarakat) Royal Vol. 6 No. 4 (2023): Oktober 2023
Publisher : STMIK Royal

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33330/jurdimas.v6i4.2092

Abstract: Because of the presence of the 4.0 Industrial Revolution, digital technology and the internet have become the backbone of technology, one of which is social media. Different types of popular social media, such as Twitter, Facebook, and instagram, serve different functions and serve different purposes. The Marketplace is a social media platform that has many advantages when used properly. Traders can sell their goods online on the marketplace by providing clear photos and descriptions. Furthermore, the payment method is made on the marketplace, on average, after the goods arrive, they pay or many call it COD (cash on delivery). Everything has changed, however, as a result of the Covid 19 pandemic. The government's health protocol restrictions have resulted in a decrease in income. The purpose of this service is to provide guidance to the Hang Nadim Batam School. The method used is by conducting a survey, then training and finally an evaluation. The results of this service showed that 80% of students could understand digital marketing technology Keywords: digital marketing; entrepreneurship; social media; pandemic covid Abstrak: Dengan hadirnya Revolusi Industri 4.0, teknologi digital dan internet menjadi tulang punggung teknologi, salah satunya media sosial. Berbagai jenis media sosial populer, seperti Twitter, Facebook, dan instagram, memiliki fungsi dan tujuan yang berbeda. Marketplace adalah platform media sosial yang memiliki banyak keuntungan jika digunakan dengan benar. Pedagang dapat menjual barangnya secara online di marketplace dengan memberikan foto dan deskripsi yang jelas. Selain itu metode pembayaran yang dilakukan di marketplace rata-rata setelah barang sampai mereka membayar atau banyak yang menyebutnya COD (cash on delivery). Namun, semuanya berubah akibat pandemi Covid-19. Pembatasan protokol kesehatan yang dilakukan pemerintah berdampak pada penurunan pendapatan. Tujuan pengabdian ini adalah untuk memberikan pembinaan pada Sekolah Hang Nadim Batam. Metode yang dilakukan dengan melakukan survey selanjutnya pelatihan dan terakhir evaluasi. Hasil dari pengbadian ini didapat bahwa siswa-siswa 80% bisa mengerti dengan teknologi digital marketing. Kata kunci: digital marketing; kewirausahaan; media sosial; pandemi covid

OTOMATISASI JEMURAN PAKAIAN MENGGUNAKAN SENSOR HUJAN BERBASIS ARDUINO VIA BOT WHATSAPP Andy; Handoko, Koko
Computer Science and Industrial Engineering Vol 13 No 1 (2025): Comasie Vol 13 No 1
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v13i1.10143

Indonesia has two seasons, namely the dry season and the rainy season. During the dry season, the need for sunlight is very much needed, one example is drying wet clothes. However, when the weather is uncertain, the job of drying clothes becomes quite troublesome and will take time and energy to lift and dry the clothes again. In this study, researchers will design a prototype that can protect clotheslines when it rains and provide notifications to the user's WhatsApp that this prototype detects rain and immediately protects the clotheslines from the rain. This prototype is designed using Arduino Uno as the main controller on this prototype and can be connected to the internet network, a rain sensor as a rain detector, and a micro servo as a roof driver above the clothesline. The results of conducting trials on the prototype are that this prototype can protect clotheslines from rain and also successfully send notifications in the form of WhatsApp messages that rain has been detected.

PREDIKSI IMPLAN GIGI MENGGUNAKAN ALGORITMA MACHINE LEARNING Zebua, Alisa; Handoko, Koko
Computer Science and Industrial Engineering Vol 13 No 2 (2025): Comasie Vol 13 No 2
Publisher : LPPM Universitas Putera Batam

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33884/comasiejournal.v13i2.10431

Advances in digital technologies, particularly artificial intelligence (AI), are transforming healthcare practices, including dental implant decision-making. This study introduces a machine learning model utilizing the Classification and Regression Tree (CART) algorithm to estimate dental implant candidacy, drawing on anonymized patient records from Ellisa Dental Clinic, Batam. The dataset comprises various demographic and clinical attributes such as age, sex, smoking patterns, bone condition, and the presence of chronic illnesses including diabetes, hypertension, and autoimmune disorders. The exploratory analysis reveals that factors like heavy smoking, systemic diseases, and jawbone integrity substantially affect implant suitability. The quality and consistency of the dataset support robust modeling. The proposed system is intended to function as a clinical decision aid, offering dentists evidence-based recommendations regarding patient eligibility. This work demonstrates the potential of predictive analytics to enhance decision accuracy and streamline dental care, contributing to the integration of AI into routine clinical workflows.

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