Agriculture, Plantation and Forestry Commodities are the main sectors supporting household daily needs and people's income for improving the economy. District of Marangkayu is located in Kutai Kartanegara area, East Kalimantan Province, where geographical condition consists of the terrain of hilly steeps surrounding the lake of Kutai Kartanegara. The geographical contours make the sector of agriculture, plantation and forestry the people's primary choice to meet the needs of household as well as increase the standard of economy of the people. In order to maintain the stability of price and production of agricultural commodities, Commodity Information System is required to provide information of the location, coordinate of positions, area of production, as well as presenting information of prices, price fluctuations and changes, along with a display of information over the accumulation of agricultural commodity production of the Kutai Kartanegara area, with additional features of appropriate distribution and production thereof. Therefore, it is necessary to develop the Web-Based Geographic Information System (GIS) for Agricultural Commodity, Plantation and Forestry of Marangkayu Area. GIS application is built using the Rapid Application Development (RAD) method, which consists of the phase of Requirements planning, User design phase, Construction phase and Cut-over phase. Database for the implementation uses PostgreSQL and PostGIS extensions. Programming language uses PHP, JavaScript, and HTML. The interface implementation is built using Bootstrap. The testing of the application uses the Black box testing method. The results of the test show that the Web-Based GIS Application has met the needs of the requirement system and the problems.
ogram Keluarga Harapan (PKH) yang berada di bawah naungan Dinas Sosial Kota Samarinda di bidang perlindungan dan jaminan sosial yang bertanggung jawab kepada Kementerian Sosial RI dalam pemberian bantuan sosial bersyarat kepada keluarga yang kurang mampu atau keluarga miskin yang terdaftar di Data Terpadu Kesejahteraan Sosial (DTKS) dan ditetapkan sebagai Keluarga Penerima Manfaat (KPM), Program Keluarga Harapan (PKH) telah menggunakan sistem e-PKH (elektronik Program Keluarga Harapan). Saat ini tata kelola Teknologi Informasi diinstansi masih belum dilakukan secara optimal, sehingga seluruh teknologi informasinya kurang terintegrasi yang mengakibatkan Teknologi Informasi belum dapat memberikan solusi atas perubahan bisnis dan aplikasi yang baik. Maka dari itu penelitian ini menggunakan pengelolaan teknologi informasi menggunakan kerangka kerja COBIT 5.0 pada domain Evaluate Direct and Monitor (EDM) yang berfokus diproses EDM01, EDM04 dan EDM05. Dari hasil perhitungan maka sistem e-PKH mendapatkan nilai tingkat kematangan secara umum mengarah kepada level 2 Defined Process.
Musik mengalami perubahan dan berevolusi hingga mencapai abad ke 21. Tidak seperti jaman purbakala, generasi digital saat ini dapat menggunakan teknologi dalam menikmati musik. Spotify menjadi salah satu aplikasi yang banyak digunakan sebagai platform dalam music streaming. Perubahan musik yang signifikan setiap tahunnya mempengaruhi pembentukan pola pikir masyarakat terhadap preferensi pilihan musik. Oleh karena itu perlu dilakukan pemantauan terhadap trend perkembangan musik serta mengetahui faktor-faktor yang mempengaruhinya untuk melihat perubahan apa saja yang terjadi serta menjadi tolak ukur untuk membantu industri musik dalam menghasilkan musik yang layak didengarkan serta membawa pengaruh positif. Penelitian ini akan memberikan hasil analisa dari perkembangan trend perkembangan musik khususnya pada aplikasi Spotify menggunakan Structured Query Language. Dari hasil analisa didapatkan visualisasi dari trend genre musik dan fitur audio dalam jangkauan tahun 2010 hingga 2020 yang diolah menggunakan Power BI. Diharapkan hasil tersebut dapat membantu indusri musik dalam menghasilkan musik yang digemari serta memberikan pengetahuan yang baik terhadap penggemar musik.Kata kunci—Musik, Spotify, SQL, Power BI
The increasing number of Android applications available on the Google Play Store with the benefits the developers get has attracted the attention of many Android application developers. To benefit from developing Android apps, one way is to know the characteristics of highly rated apps on the Google Play Store. This research will investigate the features of size, installs, reviews, type (free / paid), rating, category, content rating, and price on applications on the Google Play Store to determine the characteristics of high-rated applications. This study uses the Random Forest algorithm to identify the most influential features in high ranking applications on the Google Play Store. At the preprocessing stage, this research uses data cleaning methods and data reduction using SQL Server. This study uses feature important to find out the attributes that most influence the high ranking of Android apps on the Google Play Store. To classify high-ranking applications, the authors use 8-fold cross validation using the Random Forest algorithm and get better results than the Gradient Boost, K-NN, and Decision Tree algorithms with an accuracy of 83%. The results of the Random Forest algorithm also have better performance than the algorithm from the previous research conclusions, with a 0.8% increase in accuracy. To classify high-ranking applications, the authors use 8-fold cross validation using the Random Forest algorithm and get better results than the Gradient Boost, K-NN, and Decision Tree algorithms with an accuracy of 83%. The results of the Random Forest algorithm also have better performance than the algorithm from the previous research conclusions, with a 0.8% increase in accuracy. To classify high-ranking applications, the authors use 8-fold cross validation using the Random Forest algorithm and get better results than the Gradient Boost, K-NN, and Decision Tree algorithms with an accuracy of 83%. The results of the Random Forest algorithm also have better performance than the algorithm from the previous research conclusions, with a 0.8% increase in accuracy.
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