Systemic: Information System and Informatics Journal最新文献

{"title":"Priority Determination of Application Candidate Using Ward and Peppard’s Composite Matrix Portfolio and Business Process Analysis for Customer Relationship Management (CRM)","authors":"A. Hijriani, A. Dewi","doi":"10.29080/SYSTEMIC.V4I1.322","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V4I1.322","url":null,"abstract":"By using CRM, the organization will be able to identify of what customer requires and needs, thus will improve the service. The designing of CRM the Computer Science Department using Zachman Framework is one of the approach methods that can describe a more complex and thorough system information design. Zachman Framework has not provided a method for determining the priority of the application candidates that will be designed. Even though there are several methods to determine the priority of candidates of the information system, in this research the authors use the Ward and Peppard’s composite matrix portfolio and business process analysis for priority to determine the priority of application candidates. The phase of candidates determination by using Ward and Peppard’s composite matrix and business process is the identification of information related to Computer Science Department, SWOT analysis, business process and value chain analysis, the analysis of ISO 9001:2008 quality procedure implementation related to customer, the determination of application’s candidate, and the implementation of the questionnaire mapping of application candidate. The results are in the form of mapping for each application candidate/application system into four quadrants which are provided in composite matrix for the decision-making process. The matrix consists of key operational, strategic, high potential and support. The mapping of application candidate/information system is suitable with the contribution of each information system toward the CRM in Computer Science Department, University of Lampung. The mapping on Ward and Peppard’s composite matrix is then used to separate the application candidates into two groups, the main priority of application group/information system and non-main priority of application group/information system. There are five information systems which are classified into main priority application group/information system and recommended to be developed in further research. \u0000  \u0000 ","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134117178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis Perbandingan Sensitivitas Metode Simple Additive Weighting dan Weighted Product pada Pemilihan Kos di Sekitar UIN Sunan Kalijaga Yogyakarta","authors":"Irsalina Santi Khasanah, Agus Mulyanto, Muhammad Galih Wonoseto","doi":"10.29080/SYSTEMIC.V6I1.926","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V6I1.926","url":null,"abstract":"Metode dalam sistem pendukung keputusan sangat beragam sehingga kita perlu mengetahui metode mana yang lebih sensitif dalam penyelesaian sebuah kasus.  Penelitian ini bertujuan untuk melakukan perbandingan sensitivitas pada metode Simple Additive Weighting dan metode Weighted Product dalam pemilihan kos di sekitar UIN Sunan Kalijaga Yogyakarta. Analisis sensitivitas dilakukan pada 6 kali percobaan dengan mengubah nilai bobot mulai dari 1, 1.5, dan 2 dengan menaikkan nilai bobot sebesar 0.5 dan 0.1. Berdasarkan penelitian yang dilakukan, sensitivitas pada Metode SAW adalah 31.26% dan sensitivitas pada Metode WP adalah 30.53%. Pada penelitian ini metode SAW memiliki nilai sensitivitas yang lebih tinggi dibandingkan dengan WP.","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"123 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122876046","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analisis Usabilitas Pada Website iLab Universitas Gunadarma Menggunakan Metode Heuristic Evaluation","authors":"Ahmad Apandi","doi":"10.29080/SYSTEMIC.V6I2.918","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V6I2.918","url":null,"abstract":"Laboratorium mandiri (iLab) Univeristas Gunadarma merupakan salah satu lab yang memberikan kemudahan dalam memberikan informasi melalui website. Kemudahan dalam mengakses website tidak lepas dari usabilitas website tersebut. Usabilitas yang baik sangat berpengaruh bagi pengguna, semakin baik usabilitasnya semakin mudah pengguna menggunakan website tersebut. Agar suatu website dapat dilihat oleh pengguna dengan waktu yang cukup lama, maka usabilitas adalah syarat penting yang harus dipenuhi. Dalam penelitian ini metode yang digunakan adalah heuristic evaluation. Heuristic adalah metode evaluasi yang digunakan untuk menemukan kesalahan pada desain antar muka. Berdasarkan pengukuran website iLab menggunakan metode evaluasi heuristic oleh Nielsen didapatkan bahwa 10 aspek yang diteliti mendapatkan nilai satu dan dua, yang berarti website memiliki beberapa kekurangan yang tidak menjadikan sebuah masalah atau dengan kata lain tidak dipermasalahkan dan tidak mengganggu pengguna saat mengakses website. Nilai severity rating tertinggi yaitu sebesar 2.00 terdapat pada aspek membantu pengguna untuk mengenali, mendiagnosa, dan memulihkan dari kesalahan.","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128604532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rancang Bangun Kontrol Keasaman pH Tanaman dalam Sistem Hidroponik menggunakan Kontrol PID Berbasis Android","authors":"Ika Indriani Retna Wardani, Kunto Eko Susilo","doi":"10.29080/SYSTEMIC.V6I2.970","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V6I2.970","url":null,"abstract":"Semakin padatnya keaadan bumi dengan manusia, tentunya berpengaruh terhadap tersedianya lahan di permukaan bumi. Tentu saja ini berpengaruh pada kestabilan bercocok tanam apalagi di daerah perkotaan. Hidroponik adalah salah satu solusi pintar yang bisa diambil untuk bercocok tanam di lahan sempit atau bahkan tanpa lahan tanah. hidroponik sangat membutuhkan lingkungan yang terkontrol untuk menghindari penurunan kualitas tanaman hingga layunya tanaman. Salah satu parameter yang penting adalah derajat keasaman (pH) larutan nutrisi, dimana pH sangat berpengaruh terhadap kelarutan dan ketersediaan unsur hara dalam larutan nutrisi. Perkembangan IoT (Internet of Things) saat ini sudah mengcakup berbagai aspek salah satunya dalam dunia pertanian, dengan protokol MQTT Broker pemantauan dan pengukuran pH tanaman pada sistem hidroponik dapat dilakukan secara jarak jauh. Metode yang dilakukan adalah menggunakan PID kontrol dimana pH tanaman akan mencapai set point yang ditentukan dengan stabil. Hasil yang diharapkan adalah dapat membantu untuk melakukan kontrol pH tanaman secara jarak jauh melalui andoroid untuk mencegah penurunan kualitas tanaman.","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114563291","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Klasifikasi Text Judul Buku Perpustakaan Untuk Menentukan Kategori Buku Menggunakan K-Nearest Neighbor","authors":"Muhamad Kadafi","doi":"10.29080/SYSTEMIC.V6I2.1056","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V6I2.1056","url":null,"abstract":"The need for information in the form of books or scientific articles at the Library of UIN Raden Fatah Palembang continues to increase. To make it easier to find book information, one of which is by classifying books based on the type of category. In classifying library book data, the Nearest Neighbor Classifier method in data mining can be combined with text data extraction techniques to classify library book title text data. The purpose of this study was to classify the text title of library books using the Nearest Neighbor Classifier to determine the type of book category. This research method uses the Nearest Neighbor Classifier data mining classification technique. The results of this study are that the highest accuracy value is found at K = 12, which is 72.50%, and the model formed can be used to classify books with labels 2x0, 150, 2x2, 400, 020, 2x1, 657, 500, 375, 302.2, 800. and cannot be used for classifying books with class labels 070, 370, 330, 300, 600, 340, 700.","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"60 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130635882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Pengenalan Karakter Huruf Braille dengan Metode Convolutional Neural Network","authors":"Muhammad Fahmi Herlambang, A. Hermana, Kurnia Ramadhan Putra","doi":"10.29080/SYSTEMIC.V6I2.969","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V6I2.969","url":null,"abstract":"Karakter huruf Braille terdiri dari 6 titik yang dirancang sedemikian rupa sehingga menjadi sebuah sistem penulisan yang dapat digunakan untuk membantu tunanetra. Akan tetapi, membaca huruf Braille tidaklah mudah karena selain harus memahami huruf Braille tersebut juga dibutuhkan sensitivitas jari yang cukup agar dapat membaca huruf Braille. Adapun penelitian tentang pengenalan huruf Braille menggunakan teknologi kecerdasan buatan, salah satunya deep learning. Metode deep learning yang digunakan adalah Convolutional Neural Network (CNN). Metode CNN dapat digunakan dalam pengenalan wajah, analisis dokumen, klasifikasi gambar, dan sebagainya. Pada penelitian ini, metode CNN digunakan untuk pengenalan karakter huruf Braille. Sistem melakukan proses pengenalan karakter huruf braille per karakter dengan model yang sudah dilatih dengan dataset dengan 26 karakter. Hasil yang didapat mencapai tingkat akurasi 81.54% untuk citra karakter Braille yang diakuisisi dengan smartphone dengan kemiringan antara 0 hingga 4 derajat dan jarak 30cm dengan model training dengan learning rate 0.0001 dan optimizer Adam.","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"114 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117075891","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementasi Perbandingan Algoritma Apriori Dan FP-Growth Untuk Mengetahui Pola Pembelian Konsumen Pada Produk Panel Di PT Surya Multi Perkasa Movinko","authors":"Diego Armando Pratama Putra, Tresna Maulana Fahrudin, Natalia Damastuti","doi":"10.29080/SYSTEMIC.V6I2.963","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V6I2.963","url":null,"abstract":"Beberapa perusahaan belum banyak memanfaatkan data transaksi pembelian konsumen sebagai salah satu strategi penjualannya, data transaksi ini meliputi barang apa saja yang sering dibeli oleh konsumen dalam satu transaksi pembelian pada struk dan waktu yang berbeda.  Jika data transaksi tersebut dianalisis dan digali lebih mendalam, maka perusahaan mendapatkan suatu insight berupa analisis pola pembelian konsumen dan menguntungkan bagi perusahaan. Pada penelitian ini dilakukan analisis data transaksi pembelian konsumen menggunakan perbandingan algoritma Apriori dan FP-Growth, dimana keduanya merupakan kelompok Metode Association Rule yang bertujuan untuk mengetahui pola pembelian konsumen. Data yang digunakan pada penelitian ini diperoleh dari data transaksi pembelian produk panel pada PT Surya Multi Perkasa Movinko. Data transaksi tersebut terdiri dari 23 jenis item produk dan 492 transaksi. Hasil eksperimen dari penelitian ini menunjukkan bahwa kinerja terbaik algoritma Apriori dengan support factor sebesar 0.0054 dan confidence factor sebesar 0.30 menghasilkan 12 aturan asosiasi, sedangkan kinerja terbaik algoritma FP-Growth dengan support factor sebesar 2 dan confidence factor sebesar 0.7 menghasilkan 9 aturan asosiasi.","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127979652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implementasi Kriptografi AES untuk Keamanan Pengiriman Data Internet of Things Menggunakan Web Service Rest pada NodeMCU","authors":"Afrijal Rizqi Ramadan, Ardito Wahyu Prakoso, C GhifariDwi","doi":"10.29080/SYSTEMIC.V6I1.752","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V6I1.752","url":null,"abstract":"Munculnya istilah Internet of Things pada era ini pada umumnya mempunyai peran positif untuk manusia diberbagai aspek seperti kesehatan, perindustrian, perkotaan dan bahkan pertanian. Namun terdapat masalah yang menjadi ancaman serius dalam lingkungan IoT, yaitu tentang keamanan data. Keamanan data merupakan hal yang diharuskan dalam menyimpan atau mengirim sebuah informasi yang penting. Berbahaya jika data jatuh dan diambil oleh pihak yang nantinya dimanfaatkan dengan tidak bertanggung jawab yang nantinya dapat mengambil bahkan merubah data yang sebenarnya. Oleh karena itu, penggunaan kriptografi AES dibutuhkan untuk menyelesaikan masalah tersebut. AES singkatan dari Advanced Encryption Standard adalah algoritma enkripsi kunci berbentuk simetris yang memiliki proses keamanan data lebih cepat daripada algoritma asimetris. Tujuan penelitian ini yaitu untuk memaparkan cara menerapkan algoritma AES pada perangkat IoT sebagai upaya mengamankan data. Data berupa nilai suhu dan kelembaban yang dienkripsikan dengan algoritma AES sebelum nantinya dikirimkan kepada server basis data dengan wadah berupa jaringan nirkabel atau disebut wifi. Dalam web server data yang terenkripsi dikembalikan dengan bentuk berupa data asli sebelum masuk ke basis data.  Hasil dari pengujian ini yaitu mengkonfirmasi bahwa sistem berjalan dengan optimal. Penggunaan algoritma AES dinyatakan berhasil dalam memenuhi tujuan yaitu untuk keamanan serta kerahasiaan data.","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129804961","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Klasifikasi Multi Output pada Harga Smartphone Menggunakan Learning Vector Quantization (LVQ) dan Backpropagation (BP)","authors":"Dinita Rahmalia, Mohammad Syaiful Pradana, Teguh Herlambang","doi":"10.29080/SYSTEMIC.V6I2.967","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V6I2.967","url":null,"abstract":"There are many smartphones with various price sold in market. The price of smartphone is affected by some components such as weight, internal storage, memory (RAM), rear camera, front camera and brands. There are two methods for classifying price class of smartphone in market such as Learning Vector Quantization (LVQ) and Backpropagation (BP). From classifying price class of smartphone in market using LVQ and BP, there are the differences on the both of them. LVQ classifies price range of smartphone by euclidean distance of weight and data on its iteration. BP classifies price range of smartphone by gradient descent of target and output on its iteration. In multi output classification, one object may have multi output. Based on simulation results, BP gives the better accuracy and error rate in training data and testing data than LVQ. \u0000 ","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123638110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sistem Pembelajaran Hukum Baca Al-Qur’an Menggunakan Algoritma LPC dan KNN","authors":"Hafizh Achmad Dinan, N YoulliaIndrawaty, Kurnia Ramadhan Putra","doi":"10.29080/SYSTEMIC.V6I1.927","DOIUrl":"https://doi.org/10.29080/SYSTEMIC.V6I1.927","url":null,"abstract":"Seorang muslim harus dapat membaca ayat-ayat Al-Quran dengan baik sesuai yang diajarkan oleh Rasulullah saw. Membaca Al-Quran sesuai ilmu tajwid hukumnya wajib bagi setiap orang muslim, apabila seseorang membaca Al-Quran dengan tidak memakai tajwid, hukumnya berdosa. Pengembangan Aplikasi Pembelajaran Tajwid Al-Qur’an ini bertujuan untuk membantu seorang muslim agar pandai membaca al-Qur’an yang baik dan benar. Surat yang di pakai dalam aplikasi ini adalah surat Al-Fatihah. Aplikasi Pembelajaran Tajwid Al-Qur’an menggunakan Bahasa pemrograman Python dengan metode Linear Predictive Coding (LPC) sebagai ekstraksi ciri suara dan metode K-Nearest Neigbor sebagai pencocokan denan data latih. Untuk pengujian pengucapan ayat ke-1 didapatkan akurasi data sebesar 83.3%, ayat ke-2 sebesar 86.7%, ayat ke-3 sebesar 85%, ayat ke-4 sebesar 80%, ayat ke-5 sebesar 88.3%, ayat ke-6 sebesar 93.3%","PeriodicalId":126624,"journal":{"name":"Systemic: Information System and Informatics Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126715991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}