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All Journal SemanTIK : Teknik Informasi

Bambang Pramono

Jurusan Teknik Informatika, Fakultas Teknik, Universitas Halu Oleo, Kendari

Author-ID : 5294643

Computer Science & IT Control & Systems Engineering

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PREDIKSI TINGKAT PENYAKIT DEMAM BERDARAH DI KOTA KENDARI MENGGUNAKAN METODE MODIFIED K-NEAREST NEIGHBOR Linda Purnama Muri; Bambang Pramono; Jayanti Yusmah Sari
semanTIK Vol 4, No 1 (2018): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Kendari city is still a dengue endemic area. It is viewed from the topography of Kendari City which has a height between 0-472 m above sea level (DPL). The low ability to anticipate the occurrence of Dengue Fever is caused, among others, because the time, place and incidence rate has not been well predicted, the unavailability of index and vulnerability maps of the area based on the time of the incident, and the unavailability of predictive prediction model of DHF incidence. This underlies the need for DHF Prediction in Kendari City.The method used to predict dengue fever rate in Kendari city that is Modified K-Nearest Neighbor method. MKNN consists of two processing, the first data validation training that aims to validate training data and the second is to apply the weighting of KNN.Based on the results of testing conducted, application prediction of dengue fever (DHF) in kendari city using Modified K-Nearest Neighbor (MKNN) method is able to predict with the smallest error value 0,04%, for value k = 4 biggest error value 1 , 58 for the value of k = 4 and the smallest error rate of 0.28% for the value of k = 3.Keywords— Dengue Fever, Forecasting, Modified K-Nearst NeighborDOI :10.5281/zenodo.1402838

APLIKASI PENJADWALAN MENGGUNAKAN ALGORITMA WELCH POWELL (STUDI KASUS : SMA MUHAMMADIYAH KENDARI) Niarma Niarma; Bambang Pramono; LM Tajidun
semanTIK Vol 4, No 1 (2018): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Scheduling teaching and learning activities is a routine work performed by the Head of Curriculum Section each welcoming the new school year. The role of the Head of Curriculum Section in making the schedule of teaching and learning activities is very important and not easy because the schedule to be arranged consists of very much data. Based on the problems that exist in SMA Muhammadiyah kendari where the teacher chose the schedule of willingness to teach sendri. To assist the processing of subject schedules at SMA Muhammadiyah Kendari, it is necessary to have a scheduling information system using Welch Powell algorithm. Welch Powell algorithm is one of the methods used to solve optimization problems by performing staining based on the highest degree of Largest Degree Ordering or LDO nodes. The LDO method is considered appropriate for scheduling problems because it prioritizes the highest-level nodes that are connected by multiple patterns so they should take precedence for scheduling.By entering the necessary data, such as class data, subjects, teachers, time, school year, and willingness to teach. The system will generate lesson schedules based on teachers' willingness to teach and automatically schedule teachers who have not chosen a willingness to teach without clashing.Keywords— Welch Powell, Application, SchedulingDOI : 10.5281/zenodo.1343344

IMPLEMENTASI METODE SPREAD SPECTRUM DALAM STEGANOGRAFI PADA FILE MP3 BERBASIS ANDROID Azkar Kumala; Bambang Pramono; Rahmat Ramadhan
semanTIK Vol 3, No 2 (2017): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Kerahasiaan dan kepemilikan data saat ini menjadi rentan terhadap gangguan. Mulai dari data pribadi, data organisasi sampai data negara yang sangat rahasia Keamanan menjadi sangat menjadi penting apabila informasi dokumen yang dikirimkan merupakan informasi yang bersifat rahasia. Maka dari itu, dibutuhkan suatu cara agar keamanan dari pengiriman informasi tersebut terjamin, salah satunya adalah dengan Steganografi.Kata kunci—Steganografi, Spread Spectrum, MP3, Android.

PENERAPAN DATA MINING DENGAN METODE K-NEAREST NEIGHBOR (KNN) UNTUK MENGELOMPOKKAN MINAT KONSUMEN ASURANSI (PT.JASARAHARJA PUTERA) Wa Ode Nurhayah Kadir; Bambang Pramono; Statiswaty Statiswaty
semanTIK Vol 5, No 1 (2019): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Insurance comes from the word insurance, which means insurance. Insurance is an agreement between the insured (customer) and the insurer (insurance company). Data Mining is a way of finding hidden information in a database and is part of the Knowledge Discovery in Databases (KDD) process to find useful information and patterns in data. K-Nearest Neighbor (KNN) is a method that uses supervised algorithms where the results of newly classified query instances are based on the majority of the label classes on KNN. The purpose of the KNN algorithm is to classify new objects based on attributes and training data. The KNN algorithm works based on the shortest distance from the query instance to training data to determine the KNN. One way to calculate the short distance or distance of a neighbor using the Euclidean distance method. Euclidean distance is often used to calculate distances. Euclidean distance functions to test the size that can be used as an interpretation of the proximity of the distance between two objects.Based on the results of the testing carried out this application is able to make predictions by looking at the smallest error value. In motor vehicle insurance the smallest average value is found at k = 4 at 0.103 and the highest accuracy value is at k = 2 by 42%, personal accident insurance the smallest average value is at k = 2 at 0.116 and the highest accuracy value is at k = 2 by 67%, and fire insurance the smallest average value is at k = 2 at 0.088 and the highest accuracy value is at k = 2 at 67%.Keywords—Data Mining, Insurance, K-Nearest Neighbor DOI : 10.5281/zenodo.3116132

IMPLEMENTASI METODE TREND PROJECTION DENGAN ALGORITMA TREND LEAST SQUARE PADA SISTEM INVENTORY BARANG La Sufu; Bambang Pramono; Natalis Ransi
semanTIK Vol 6, No 1 (2020): semanTIK
Publisher : Informatics Engineering Department of Halu Oleo University

Permasalahan yang terjadi di PT. Pinus Merah Abadi Kendari pada kegiatan persediaan barang adalah jumlah barang yang harus di pesan ke pabrik tidak menentu karena ketidakpastian informasi barang yang tersedia digudang. Oleh karena itu, PT. Pinus Merah Abadi Kendari memerlukan suatu alat bantu berupa perangkat lunak atau sistem yang dapat membantu memudahkan dan memaksimalkan kinerja pegawai administrasi gudang atau manajer perusahaan dalam meramalkan atau memprediksi jumlah barang yang harus di pesan ke pabrik untuk periode berikutnya. Untuk memudahkan dalam memprediksi persediaan barang tersebut maka dibuat penelitian yang bertujuan untuk membangun sebuah sistem yang dapat memprediksi penjualan barang dengan cara mengimplementasikan metode Trend Projection.Trend Projection merupakan metode peramalan yang digunakan untuk melihat trend dari data deret waktu yang diketahui melalui persamaan Trend Least Square. Data yang digunakan merupakan data penjualan barang PT. Pinus Merah Abadi Kendari. Jenis produk yang digunakan Nabati RCE 8-gram yaitu penjualan dari Bulan Januari 2016 sampai Desember 2018. Berdasarkan hasil analisis dan pengujian sistem, maka sistem ini dapat meramalkan penjualan produk nabati dibulan tertentu dengan syarat data yang digunakan minimal tiga periode.Kata kunci; Trend Projection, Trend Least Square, Persediaan Barang

Co-Authors Azkar Kumala Jayanti Yusmah Sari La Sufu Linda Purnama Muri LM Tajidun Natalis Ransi Niarma Niarma Rahmat Ramadhan Statiswaty Wa Ode Nurhayah Kadir

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