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Database plays an important role on both web-based or desktop based academic information system (AIS) in Indonesian higher education institutions (HEI). Nowadays webbased AIS dominates inIndonesian HEI, almost every HEI uses web-based AIS with relational database management system (RDBMS) as database software. Relational database systems such as Oracle, MySQL, MS SQL Server or PostgreSQL are familiarly used as database management system in the AIS. There are many researches on development of AIS in HEI but none of them is discussing database security and integrity. This research will perform the analysis of database security model that could be used in AIS such as table constraints, table relationships and role-based access control (RBAC).

Emosi memenuhi kehidupan manusia setiap waktu. Emosi mempengaruhi hubungan sosial, ingatan dan bahkan dalam pengambilan keputusan. Saat ini, orang cenderung mengekspresikan emosi melalui media sosial seperti Facebook dalam bentuk gambar, video dan teks pada umumnya. Deteksi emosi pada teks merupakan bidang penelitian yang baru dan banyak diteliti khususnya dibidang linguistik. Penelitian ini menggunakan EmoLex sebagai leksikon yang digunakan untuk mendeteksi emosi pada suatu teks. Kosa kata pada EmoLex diperluas dengan pencarian sinonim menggunakan Kateglo API. EmoLex digunakan sebagai leksikon 8 kategori emosi Plutchik dan sentimen. EmoLex tersedia dalam 105 bahasa berbeda termasuk Indonesia yang mana mengandung 14.182 kata yang kemudian diperluas dengan pencarian sinonim menggunakan Kateglo API. Pencarian sinonim menghasilkan 20.690 kata sehingga memperoleh hasil akhir leksikon emosi yang berisi 34.872 kata. Pengujian menunjukkan bahwa leksikon emosi mampu mendeteksi 55.45% atau 15.357 dari 27.696 kata yang diperoleh dari update status pengguna Facebook dalam melakukan pendeteksian emosi, sebanyak 100 update status diambil dari Facebook. Selanjutnya update status tersebut diperbaiki menggunakan Natural Language Processing (NLP). Hasil perbaikannya dinilai dengan leksikon emosi yang telah dibuat sebelumnya. 26 dari 100 update status dapat diketahui label emosinya. Hasil validasi terdapat 16 update status atau 61,53% label emosinya akurat.

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PurposeGathering knowledge regarding personality traits has long been the interest of academics and researchers in the fields of psychology and in computer science. Analyzing profile data from personal social media accounts reduces data collection time, as this method does not require users to fill any questionnaires. A pure natural language processing (NLP) approach can give decent results, and its reliability can be improved by combining it with machine learning (as shown by previous studies).Design/methodology/approachIn this, cleaning the dataset and extracting relevant potential features “as assessed by psychological experts” are essential, as Indonesians tend to mix formal words, non-formal words, slang and abbreviations when writing social media posts. For this article, raw data were derived from a predefined dominance, influence, stability and conscientious (DISC) quiz website, returning 316,967 tweets from 1,244 Twitter accounts “filtered to include only personal and Indonesian-language accounts”. Using a combination of NLP techniques and machine learning, the authors aim to develop a better approach and more robust model, especially for the Indonesian language.FindingsThe authors find that employing a SMOTETomek re-sampling technique and hyperparameter tuning boosts the model’s performance on formalized datasets by 57% (as measured through the F1-score).Originality/valueThe process of cleaning dataset and extracting relevant potential features assessed by psychological experts from it are essential because Indonesian people tend to mix formal words, non-formal words, slang words and abbreviations when writing tweets. Organic data derived from a predefined DISC quiz website resulting 1244 records of Twitter accounts and 316.967 tweets.

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