Micro, Small and Medium Enterprises (MSMEs) have very significant contribution to the growth and development of the economy of Bekasi City with a total of approximately 203,000 units. The large number of food processing MSMEs in Bekasi City has not been accompanied by the formation of a spatially integrated MSMEs center zoning. This research aims to understand the spatial distribution and the determination of the zones of food processing MSMEs in Bekasi City. The research was conducted in Bekasi City during February-August 2019. Data were obtained through data tracing from related agencies, field observations, and interviews with experts. Analytical methods include Analytical Hierarchy Process (AHP) to determine the weight of each parameter, and Multi Criteria Evaluation (MCE) for determining development centers. Analysis shows that the number of selected MSMEs was 220 samples, with the highest number of MSMEs in Pondok Gede District. Food processing MSME development zones in Bekasi City are divided into three development zones, namely development zone 1, development zone 2, and development zone 3. Development zone 1 and development zone 2 are the best zones located in West Bekasi District, Jatiasih District, and Jatisampurna District. Development Zone 2 consists of North Bekasi District, Medan Satria District, Pondok Gede District, and Pondok Melati District because the two zones are adjacent to Jakarta City. Meanwhile, development zone 3 should receive special attention, consisting of Bantar Gebang District, South Bekasi District, East Bekasi District, Mustika Jaya District, and Rawalumbu District.
The development of autonomy University drives management innovation to increase the alternative sources of income with the purpose of the efficiency improvement and productivity of the institution. One of a management model that leads to increase productivity through cost reduction is Lean service. The implementation of Lean Higher Education Institution (LHEI) requires total involvement of organization maneuver, including social culture, infrastructure, and leadership support. Therefore, the readiness of the institution in welcoming Lean concepts becomes significant. This article tried to develop a prototype of an intelligent performance measurement tool by analyzing the readiness indicators using the Analytical Hierarchy Process (AHP) method. This tool provided the classification of organizational readiness into five performances level. The measurement performed as a Decision Support System (DSS) to recommend University management level in making a decision and correcting action towards the optimal execution of Lean service. As a case study, this prototype system has been tested with Black Box and User Acceptance Test (UAT) in Indonesia Islamic Higher Education Institution. The finding reveals that the prototype system can be used as a performance measurement tool in measuring the readiness of Lean's service in Islamic Higher Education Institution.
This study focuses on the development of an expert system that contains the knowledge of an expert who is believed to be the truth so as to help network officers to identify and address the disruption of Internet network services quickly and accurately. The stages of the expert system developed are the identification of internet network problems, the search for sources of knowledge, the acquisition of knowledge with 10 interruptions and 21 causes of interference from experts, knowledge representation, inference engine development using j48 classification algorithms with the help of weka applications, and system implementation. The system has been successfully created with an inference engine accuracy of 91.8%. Keywords: Expert System, Internet Network Problems, J48, Weka A. PENDAHULUANInternet merupakan kebutuhan yang memiliki peranan sangat penting saat ini untuk digunakan oleh berbagai pihak. Salah satunya adalah pihak yang bergerak di dalam dunia pendidikan yaitu perguruan tinggi. Di Indonesia sendiri hampir semua perguruan tinggi baik negeri maupun swasta telah memanfaatkan internet untuk mendukung dan melancarkan sebagian besar aktifitas akademik maupun nonakademiknya.Institut Pertanian Bogor (IPB) adalah salah satu perguruan tinggi di Indonesia yang telah memanfaatkan internet untuk mendukung segala aktifitasnya. Beberapa pemanfaatan layanan internet untuk aktifitas yang ada di IPB antara lain layanan untuk informasi penerimaan mahasiswa baru, penentuan pengisian rencana studi, repository online, perpustakaan, sistem kepegawaian, blog, payroll, dan sebagainya. Besarnya pemanfaatan dan pengaruh layanan internet bagi civitas akademika IPB semakin mendorong IPB untuk selalu dapat meningkatkan kualitas layanan internet baik dari segi infrastruktur jaringan, manajemen data maupun pengembangan aplikasi.Sebuah sistem jaringan yang kompleks seperti yang terdapat di institusi pendidikan akan melibatkan begitu banyak komponen di dalamnya. Untuk mengatur agar seluruh sistem berjalan dengan baik diperlukan petugas jaringan yang mampu mengatasi permasalahan yang sering muncul. Petugas jaringan sangat penting kedudukannya dalam hal pemeliharaan (maintenance) jaringan internet. Petugaspetugas tersebut harus dapat memahami dan menguasai karakteristik jaringan internet dan mampu memperbaiki segala kerusakan yang terjadi.Salah satu permasalahan yang sering dialami oleh pengguna layanan internet di IPB adalah gangguan layanan jaringan internet seperti akses internet yang tiba-tiba
Abstrak - Data mining merupakan teknik menggali informasi baru dari gudang data, informasi sangat penting dan berharga karena dengan menguasai informasi maka dengan mudah mencapai sebuah tujuan, hal ini membuat setiap orang berlomba untuk memperoleh informasi, demikian juga pada bidang kesehatan terkhusus yang diteliti penulis yaitu penderita hipertensi. Hipertensi merupakan penyakit tidak menular yang prevalensinya sebesar 22% pada kelompok usia 18 tahun pada 2014 dan terus meningkat serta mampu meningkatkan risiko penyakit jantung koroner sebesar 12% dan meningkatkan risiko stroke sebesar 24%. Kebanyakan gejala yang dialami penderita tidak dapat dideteksi secara dini. Karenanya, perlu dilakukan penelitian dalam mendiagnosa pola perilaku dan gaya hidup terhadap penderita hipertensi menggunakan metode algoritma apriori. Data yang didapatkan melalui penyebaran kuisioner di puskesmas Melur dan rumah sakit Aulia Hospital. Atribut yang digunakan pada penelitian ini adalah jenis kelamin, usia, kebiasaan merokok, kebiasaan mengkonsumsi alkohol, intensitas aktifitas fisik, olahraga, dan pola konsumsi makanan. Pada pengujian parameter algoritma apriori dalam mencari pola dengan melihat hasil nilai support dan confidence pada metode algoritma apriori. Pengujian penelitian ini menggunakan tools Tanagra versi 1.4. Dari pengujian 300 data penderita hipertensi menggunakan nilai support 30% dan confidence 85% ditemukan 6 pola/rules dengan lift ratio ≥1.Kata kunci: Hipertensi, Diagnosa, Algoritma apriori, support, confidence, lift ratio Abstract - Data mining is a technique to dig new information from the data warehouse, information is very important and valuable because by mastering information, it is easy to achieve a goal, this makes everyone compete to obtain information, as well as in the field of health, especially those studied by the author, namely people with hypertension. Hypertension is a non-communicable disease whose prevalence was 22% in the age group of ≥ 18 years in 2014 and continues to increase and is able to increase the risk of coronary heart disease by 12% and increase the risk of stroke by 24%. Most of the symptoms experienced by sufferers cannot be detected early.Therefore, it is necessary to conduct research in diagnosing behavioral patterns and lifestyles for hypertension patients using the a priori algorithm method. The data obtained through the distribution of questionnaires at the Melur health center and Aulia Hospital. The attributes used in this study were gender, age, smoking habits, alcohol consumption habits, intensity of physical activity, exercise, and food consumption patterns. In testing the parameters of the a priori algorithm, it is produced in looking for patterns by looking at the results of support and confidence values in the a priori algorithm method. Testing this study using Tanagra tools version 1.4. From testing 300 data on hypertension patients using support values of 30% and confidence of 85% found 6 patterns / rules with an lift ratio of ≥1.Keywords: Hypertension, Diagnosis, Apriori algorithm, support, confidence, lift ratio
Abstrak - Ujaran kebencian semakin meningkat bersamaan dengan banyaknya pengguna media sosial. Twitter merupakan salah satu media sosial yang membantu penyeberan ujaran ujaran melalui fitur twit-nya yang dilakukan berulang-ulang. Penelitian ini dilakukan untuk mengklasifikasi apakah sebuah twit mengandung ujaran kebencian atau bahasa kasar, dan jika terdeteksi mengandung ujaran kebencian maka akan diukur tingkatannya. Dataset yang digunakan diambil dari twitter sebanyak 13.126 twit asli. Klasifikasi menggunakan Algoritma logistic Regression dan fitur teks word embedding. Dilakukan beberapa kali percobaan untuk mendapatkan model terbaik agar pengujian didapatkan secara optimal. Rata-rata akurasi yang dari ketiga kelas sebesar 75,59%, untuk kelas hate speech 75,86%,kelas abusive 80,05%, kelas level 70,86% dengan komposisi 90:10.Kata kunci: Klasifikasi, Logistic Regression, Ujaran Kebencian, Twitter. Abstract - Hate speech is increasing along with the number of social media users. Twitter is one of the social media that helps spread utterances through its repeated tweet features. This study was conducted to classify whether a tweet contains hate speech or abusive language, and if it is detected to contain hate speech, the level will be measured. The dataset used was taken from twitter as many as 13,126 original tweets. Classification using Logistic Regression Algorithm and word embedding text feature. Several experiments were carried out to get the best model so that the test was obtained optimally. The average accuracy of the three classes is 75.59%, for the hate speech class is 75.86%, the abusive class is 80.05%, the level class is 70.86% with a composition of 90:10.Keyword : Classification, Logistic Regression, Hate Speech, Twitter.
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