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All Journal Jurnal Pengabdian UntukMu NegeRI Sinkron : Jurnal dan Penelitian Teknik Informatika Jurnal Pilar Nusa Mandiri VOLT : Jurnal Ilmiah Pendidikan Teknik Elektro JITK (Jurnal Ilmu Pengetahuan dan Komputer) J-SAKTI (Jurnal Sains Komputer dan Informatika) Jurnal Riset Informatika Journal of Information System, Applied, Management, Accounting and Research Jurnal AbdiMas Nusa Mandiri JPM: JURNAL PENGABDIAN MASYARAKAT Abditeknika - Jurnal Pengabdian Kepada Masyarakat J-SAKTI (Jurnal Sains Komputer dan Informatika) Tridharmadimas: Jurnal Pengabdian Kepada Masyarakat Jayakarta SPEED - Sentra Penelitian Engineering dan Edukasi Jurnal Esensi Infokom: Jurnal Esensi Sistem Informasi dan Sistem Komputer PROSISKO : Jurnal Pengembangan Riset dan observasi Rekayasa Sistem Komputer Jurnal Pengabdian Kreatif Cemerlang Indonesia Vertex Repeater: Publikasi Teknik Informatika dan Jaringan
Tuslaela
Universitas Nusa Mandiri
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Journal : Jurnal Pilar Nusa Mandiri

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KOMPARASI METODE KLASIFIKASI PADA ANALISIS SENTIMEN USAHA WARALABA BERDASARKAN DATA TWITTER Mardiana, Tati; Syahreva, Hafiz; Tuslaela, Tuslaela
Jurnal Pilar Nusa Mandiri Vol 15 No 2 (2019): Pilar Nusa Mandiri : Journal of Computing and Information System Periode Septemb
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1039.919 KB) | DOI: 10.33480/pilar.v15i2.752

Abstract

Saat ini usaha waralaba di Indonesia memiliki daya tarik yang relatif tinggi. Namun, para pelaku usaha banyak juga yang mengalami kegagalan. Bagi seseorang yang ingin memulai usaha perlu mempertimbangkan sentimen masyarakat terhadap usaha waralaba. Meskipun demikian, tidak mudah untuk melakukan analisis sentimen karena banyaknya jumlah percakapan di Twitter terkait usaha waralaba dan tidak terstruktur. Tujuan penelitian ini adalah melakukan komparasi akurasi metode Neural Network, K-Nearest Neighbor, Naïve Bayes, Support Vector Machine, dan Decision Tree dalam mengekstraksi atribut pada dokumen atau teks yang berisi komentar untuk mengetahui ekspresi didalamnya dan mengklasifikasikan menjadi komentar positif dan negatif. Penelitian ini menggunakan data realtime dari tweets pada Twitter. Selanjutnya mengolah data tersebut dengan terlebih dulu membersihkannya dari noise dengan menggunakan Phyton. Hasil pengujian dengan confusion matrix diperoleh nilai akurasi Neural Network sebesar 83%, K-Nearest Neighbor sebesar 52%, Support Vector Machine sebesar 83%, dan Decision Tree sebesar 81%. Penelitian ini menunjukkan metode Support Vector Machine dan Neural Network paling baik untuk mengklasifikasikan komentar positif dan negatif terkait usaha waralaba.
DECISION SUPPORT SYSTEM SELECTION OF THE BEST ANDROID SMARTPHONE USING THE METHOD OF MOORA Ernawati, Siti; Al Hakim, Imam Taftazani; Tuslaela, Tuslaela
Jurnal Pilar Nusa Mandiri Vol 16 No 2 (2020): Pilar Nusa Mandiri : Journal of Computing and Information System Publishing Peri
Publisher : LPPM Universitas Nusa Mandiri

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33480/pilar.v16i2.1667

Abstract

In the current conditions of each person must have a smartphone due to a lot of activities are done online. These activities can be in the form of learning, purchasing, transportation, and so forth. Smartphones offered currently have various specifications, sometimes prospective buyers feel confused to choose a smartphone as what they need. To overcome the problems in the decision of the selection of the best android smartphone that is with the decision support system using the method of Multi-Objective Optimization based on Ratio Analysis (MOORA). In this study, the data collected based on the 100 questionnaires that were distributed. The criteria used, namely random access memory (RAM), camera, price, storage capacity, battery life, and screen size. The results of the calculation obtained in this study determine each brand and type of smartphone the best android. Expected in this study can help prospective buyers who are confused in choosing the best android smartphone
Co-Authors Abdul Rahman Kadafi Abdul Rahman Kadafi Abdul Rahman Khadafi Al Hakim, Imam Taftazani Anneke Indra Kirana Cahyani Budihartanti Cahyani Budihartanti Cahyani Budihartanti Cahyani Budihartanti D. Desmira Dannys Permadi Dinda Juliana Ega Maulana Ditama Eka Kusuma Pratama Elok Nur Aeni Embun Fajar Wati Enok Tuti Alawiah Ernawati, Siti Faqih Muhammad Farhan Rahmadhani Helina Apriyani Ihsanurrijal Muwahid Indah Purnamasari Indah Purnamasari Indah Purnamasari INDAH PURNAMASARI Ino, Yoseph Adrianus Ishak Kholil Ishak Kholil Istikharoh, Istikharoh Kadafi, Abdul Rahman Laila Septiana Muhammad, Faqih Muwahid, Ihsanurrijal Nazarius, Jerry Kristian Rahmadhani, Farhan Ricardus Mba Dala Pati Rusdiansyah , Rusdiansyah Rusdiansyah Rusdiansyah Santoso Setiawan Siti Ernawati - STMIK Nusa Mandiri Sri Rusiyati Sri Rusiyati Suharyanti , Nining suharyanti, Nining Supendar , Hendra Syahreva, Hafiz Tati Mardiana Tati Mardiana, Tati Yopi Handrianto, Yopi Yoseph Adrianus Ino Yumi Novita Dewi Yunan Maulana Putra