The current Covid-19 pandemic has led to an increase in the poverty rate in Indonesia. To overcome the decline in income, the Ministry of Social Affairs provides Cash Social Assistance (BST) to 9 million KPM in Indonesia. One of the villages that received cash social assistance is Keramas Village. The problem is that the BST recipient is not precise. The purpose of this study is to classify the eligibility of BST recipients using the C4.5 algorithm and the C4.5 algorithm is expected to be able to provide recommendations for decision making in receiving other assistance. The results of this study are to predict whether the community is eligible or not eligible to receive BST. Researchers tested using 5 attributes, including having received other assistance, employment, education, having a poor card, and number of dependents. The test uses 2,074 data which is divided into 2 types of data, namely 80% training data and 20% testing data. This test produces an accuracy of 97.83% and compares with the K-Nearest Neighbor algorithm which produces an accuracy of 92.29% and Naïve Bayes of 91.81%.
STMIK Primakara is a Technopreneurship campus who have a vision to produce IT Scholar with entrepreneurial mentality. However, to achieve that, STMIK Primakara need to manage the IT Government to support the academics and non-academics environment around the students until graduates are controlled by department of IT Development and Implementation (PPTI). The researcher will evaluates the capability level of IT Governance in STMIK Primakara and provide recommendations for the gap between the performance in domain process APO03, APO04, and BAI01 using quantitative and qualitative methods. Moreover, the researcher will use survey, interview, and document studies for the data required. The results of this research shows the average of capability level in domain process APO03, APO04, and BAI01 are partially achieved. Therefore, to fill the gap between STMIK Primakara current capability level and expected capability level, recommendation have been given by the researcher to create an improvement regarding to STMIK Primakara IT Governance.
Digital startups are more cutting edge for the young entrepreneurs in this year and always growing in numbers (1). This cause is affected change in business patterns in Indonesia, including Bali. This research aims about how to create a digital business with Lean Startup Machines and Questionnaire User Experience method (UEQ). This business originated from the fact that careers women in Bali had difficulty to buy traditional ceremonial equipment. We developed the early stage of software startup model. First of all, we must concept our ideas and manage it before provides clear criteria and then product ideas. Lean startup model is designed to minimize the risk of returns from products made for market needs. One of tools in this method for customer validation is the validation board. From the research obtained from the core assumptions to survey directly prospective customers for the minimum success criteria that are required. This research had 70% of success required which up to 3 times for the pivot. While the results of the Questionnaire User Experience method (UEQ) the opinion of the community about the system that had made get the overall average impression of Attractiveness (1.71), Persicuity (1.35), Efficiensy (2.66), Dependability (0.92), Stimulation (1.28) dan Novelty (0.69).
Jumlah kerugian yang disebabkan oleh kebakaran terus meningkat setiap tahun. Peningkatan jumlah kerugian ini disebabkan karena meningkatnya jumlah kebakaran yang terjadi saat musim semi. Penyebab utama kebakaran yang terjadi di Indonesia karena suhu yang relatif panas, kemudian kurangnya metode deteksi dan evakuasi yang cepat setiap kebakaran terjadi. Pemanfaatan teknologi dengan basis Internet of Things (IoT) dapat diterapkan untuk mencegah potensi terjadinya kebakaran di Indonesia. Kecerdasan yang dimiliki oleh perangkat IoT dan kemampuan untuk mengirimkan data secara periodik menjadi salah satu faktor pendukung perangkat ini dapat digunakan guna mencegah potensi kebakaran. Dalam penelitian ini dilakukan pengembangan berupa prototyping terhadap sistem yang mampu melakukan deteksi dan secara otomatis melakukan pemadaman jika terdeksi adanya kebakaran. Dari penelitian ini didapatkan bahwa tingkat responsivitas yang dimiliki oleh alat pemadam kebakaran berbasis IoT ini menjadi lebih akurat jika dilakukan dengan menggunakan kombinasi dari beberapa jenis sensor, seperti sensor MQ2 dan sensor LDR. Besaran optimal sensitivitas perangkat pendeteksi dengan kombinasi sensor adalah sebesar 9ms dan nilai delay pengiriman data yang dialami 1257,4ms.
Penelitian merupakan salah satu unsur yang wajib dilakukan baik oleh dosen maupun mahasiswa di perguruan tinggi. Dalam hal ini memungkinkan para peneliti mengambil topik yang sama atau hampir serupa. Melalui penelitian ini akan dilakukan analisis untuk mengelompokkan dokumen penelitian. Hasil dari pengelompokan dokumen penelitian ini akan memperlihatkan bagaimana pola kemiripan dan keterkaitan antar penelitian dalam bentuk cluster. Data yang digunakan dalam penelitian ini adalah judul penelitian dosen tahun 2019-2021 berumlah 52 data. Proses ekstraksi dokumen dilakukan dengan menggunakan proses text mining, sedangkan untuk proses pengelompokan dokumen dilakukan dengan menggunakan metode k-means clustering dengan cosine similarity. Pada tahap text mining dilakukan proses preprocessing diantaranya proses tokenization, filtering dan stemming. Algoritma K-Means mampu menghasilkan cluster optimal yang berjumlah 6 cluster. Trend topik penelitian yang dilakukan dosen di STMIK Primakara meliputi Pengembangan dan Evaluasi Sistem Informasi, E-Government, Data Mining, Teknologi Pendidikan, Machine Learning/Artificial Intelligence, serta Manajemen dan Bisnis.
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